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Cracks-and-Potholes-in-Road-Images-Dataset/LICENSE

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+MIT License
+
+Copyright (c) 2020 Bianka Passos
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Cracks-and-Potholes-in-Road-Images-Dataset/README.md

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+## Cracks and Potholes in Road Images Dataset
+
+### Abstract 
+
+The poor condition of the roads directly affects traffic safety. In several countries, vehicles that survey road information are already used. These vehicles capture information and images that are used to define highway intervention and maintenance strategies. The images captured by these vehicles allow the identification of problems found on the highways, however in many cases they need manual analysis by trained technicians. 
+
+Defects such as cracks and potholes can be identified automatically using image processing and machine learning techniques. Developed researches in the field of machine learning requires a large set of images, whether for training the algorithms or during the recognition test. In this context, this dataset was created containing images of defects in asphalted roads in Brazil, in order to be used for a study on the detection of cracks and potholes in asphalted roads, using texture descriptors and machine learning algorithms such as Support Vector Machine, K-Nearest Neighbors and Multi-Layer Perceptron Neural Network. 
+
+The dataset was developed using images made available by Brazilian National Department of Transport Infrastructure (NDTI), through the Access to Information Law - Protocol 50650.003556/2017-28. The images are from highways in the states of Espírito Santo, Rio Grande do Sul and the Federal District. 2235 images were selected manually, following criteria such as not showing signs of vehicles and people, as well as not having image defects. This work consists of 2235 samples of roads where each image has 3 masks that delimit the vehicle's path and crack and pothole defects.
+
+### Keywords
+
+Road, Pavement, Defects, Detection, Recognition, Crack, Pothole
+
+### Authors
+
+Bianka Tallita Passos, Mateus Junior Cassaniga, Anita Maria da Rocha Fernandes, Kátya Balvedi Medeiros, Eros Comunello
+
+### Affiliations
+
+University of Itajaí Valley – UNIVALI. Santa Catarina – Brazil.
+
+### Value of Data
+
+- The data can be used to train classifiers or artificial neural networks to identify a lane, cracks or potholes. Different classifiers can be trained to identify the best type of image for a given problem, such as defects recognition for road maintenance.
+- The federal road network in several countries is monitored using vehicles that capture images. However, the analysis and marking of the defects found in these images is done posteriori, by technicians in laboratories. A tool capable to detect and classify defects in these images in an automated way could replace this step, speeding up the process and reducing the final cost of monitoring.
+- Vision-based methods, which use image processing to detect defects in the floor, enables a low investment cost and can be achieved with common cameras.
+- Cracks and potholes are types of defects in the pavements that can compromise the safety and quality of the roads. The identification of such defects is an important step for intervention and maintenance strategies to be carried out.
+
+### Data Description
+
+The dataset images were extracted from videos captured by NDTI, using a Highway Diagnostic Vehicle (HDV). To register the highways, the HDV is equipped with a high-resolution camera and two cameras for filming.
+
+The camera is installed on the highest part of the vehicle, facing the front and with an inclination closer to orthogonality. Thus, the visibility of the pavement is 15 meters. This camera captures images with a minimum resolution of 4 megapixels, every 5 meters away [1].
+
+The video cameras, installed on the front and rear of the HDV, are responsible for the continuous capture of videos with a rate of 30 Frames Per Second (FPS). The resolution is at least 1280x729 and respects the 16:9 aspect ratio. Figure 1 shows the main characteristics of the
+HDV [1].
+
+![](/figures/1.png)
+Figure 1. Representation of the HDV used by NDTI [1]: (a) satellite tracking system (b) high-resolution camera (c) recording cameras (d), precision odometer and (e) laser sensors.
+
+The images were provided by the NDTI on a hard disk, and with the following characteristics:
+- The images were captured between 2014 and 2017; and
+- They are images from highways of Espírito Santo state (BR 101, 259, 262, 393, 447, 482 and 484), Rio Grande do Sul state (BR 101, 290 and 386) and Federal District (BR 010, 020, 060, 070, 080 and 251).
+
+The dataset was developed using only the images provided by NDTI. A total of 2235 images were selected manually, considering the following criteria:
+1. To count as an image with damaged asphalt, present crack(s) and/or pothole(s);
+2. Do not contain vehicles in images;
+3. Do not contain people in images; and
+4. No problems due to capture, such as defects in colors (colors that do not correspond to the rest of the image) and defects in the image (such as missing parts).
+
+Figure 2 shows some images present in this database.
+
+![](/figures/2.png)
+
+Figure 2. Example of some images from the dataset.
+
+Each image has 3 masks - binary images in PNG (Portable Network Graphics) format - separated for each type of annotation: road, crack and pothole. The annotation of the road consisted of demarcating the total region corresponding to the vehicle's road, as shown in Figure 3.
+
+![](/figures/3.png)
+
+Figure 3. Road region annotation example.
+
+The annotation of cracks and potholes consisted of the defect selection, maintaining its shape as much as possible, as shown in Figure 4. 
+
+![](/figures/4.png)
+
+Figure 4. Pothole annotation example (blue) and cracks (red).
+
+Figure 5 shows the separate masks for each type of annotation - road, pothole and crack - that compose this dataset.
+
+![](/figures/5.png)
+
+Figure 5. Example of the original image (a) and the masks corresponding to the road region (b), pothole (c) and crack (d).
+
+For the identification of cracks and potholes, the same definitions presented in NDTI [2] and Fernandes, Oda and Zerbini [3] were used.
+
+### Experimental Design, Materials and Methods
+
+The images provided by NDTI have no labelling or any information referring to the damage present on the roads. A tool was developed that made it possible to annotate these objects/defects in order to create the ground-truth available in this article. The tool received as input the original image, where it is possible to select the region of the road and the defects found. As a result, the marks generated were converted into masks.
+
+### Acknowledgments
+
+This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nı́vel Superior – Brasil (Higher Level Personnel Improvement Coordination - Brazil - CAPES) - Finance Code 001.
+
+### References
+
+[1] Brazil. Departamento Nacional de Infra-Estrutura de Transportes (National Department of Transport Infraestructure) . Edital Pregão Eletrônico No 0268/16-00, 2016.
+
+[2] NDTI. Norma DNIT 005/2003 - TER: Defeitos nos pavimentos flexíveis e semi-rígidos. Rio de Janeiro. 2003.
+
+[3] Fernandes Jr, J. L.; Oda, S.; Zerbini, L. F. Defeitos e atividades de manutenção e reabilitação em pavimentos asfálticos. Universidade de São Paulo: Escola de Engenharia de São Carlos. São Paulo (SP), 1999.
+
+### DOI
+
+10.17632/t576ydh9v8.4
+
+### Cite this dataset
+
+[Passos, Bianka T.; Cassaniga, Mateus J.; Fernandes, Anita M. R. ; Medeiros, Kátya B. ; Comunello, Eros (2020), “Cracks and Potholes in Road Images”, Mendeley Data, V4, doi:10.17632/t576ydh9v8.4](http://dx.doi.org/10.17632/t576ydh9v8.4) 
+
+### Corresponding author(s)
+
+[Bianka Passos](mailto:biankatpas@gmail.com)
+
+### Download
+[Mendeley Data](
+https://data.mendeley.com/datasets/t576ydh9v8/3/files/afc7c028-06e0-475b-b190-e008df681b19/Cracks-and-Potholes-in-Road-Images.zip?dl=1
+)

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arm-model/.gitignore

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+.vscode/
+__pycache__/
+*.pyc
+saved_models/
+outputs/
+*.tflite
+*.onnx

arm-model/README.md

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+# Road Anomaly Detection — YOLO + CNN‑BiGRU
+
+This repository contains a starter implementation of a YOLO-based detector combined with a CNN + Bidirectional GRU temporal classifier (CNN‑BiGRU) for road anomaly assessment, inspired by "An intelligent YOLO and CNN‑BiGRU framework for road infrastructure based anomaly assessment".
+
+Contents:
+- `scripts/train_detector.py` — train YOLO detector (Ultralytics YOLOv8 recommended)
+- `scripts/train_bigru_tf.py` — train CNN‑BiGRU temporal model (TensorFlow/Keras)
+- `scripts/convert_to_tflite.py` — convert Keras model to TFLite (int8 quantization)
+- `scripts/detect.py` — inference pipeline combining detector + temporal model
+- `scripts/benchmark.py` — simple FPS/latency benchmark utility
+- `model/temporal/cnn_bigru.py` — Keras model builder for CNN + BiGRU
+- `config.yaml` — default configuration for experiments
+
+Quick start
+1. Create a Python environment and install requirements:
+
+   pip install -r requirements.txt
+
+2. Train detector (example):
+
+   python scripts/train_detector.py --data data/yolov8_data.yaml --epochs 50
+
+3. Train temporal model (example):
+
+   python scripts/train_bigru_tf.py --data_dir data/sequences --epochs 30
+
+4. Convert temporal model to TFLite (int8):
+
+   python scripts/convert_to_tflite.py --model_path saved_models/bigru.h5 --repr_dir data/representative
+
+5. Run inference on a video:
+
+   python scripts/detect.py --video sample_video.mp4
+
+Notes
+- This scaffold targets reproducible experimentation and Pi‑friendly deployment. The temporal model is implemented in TensorFlow/Keras for easier TFLite conversion.
+- The detector training uses Ultralytics YOLOv8 (PyTorch). Converting YOLO to TFLite is possible via ONNX/TF export and is left as a conversion step in `scripts/convert_to_tflite.py`.
+
+License: Add your license

arm-model/config.yaml

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+detector:
+# Default project configuration
+detector:
+  model: yolov8n.pt
+  input_size: 320
+  conf_thresh: 0.35
+  iou_thresh: 0.45
+  epochs: 50
+  batch: 16
+  lr: 0.002
+  optimizer: auto
+  weight_decay: 0.0005
+  augment: true
+  mosaic: true
+
+temporal:
+  seq_length: 8
+  input_size: 224
+  num_classes: 2
+  threshold: 0.5
+  epochs: 30
+  batch: 8
+  lr: 0.001
+  dropout: 0.4
+  projection_dim: 256
+  gru_units: 128
+
+inference:
+  threads: 4
+  save_clip_seconds: 5
+  min_detection_frames: 2
+
+representative:
+  samples: 200
+
+smoke:
+  detector_epochs: 3
+  detector_batch: 8
+  temporal_epochs: 5

arm-model/model.py

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+"""
+model.py — Unified model definitions for Road Anomaly Detection.
+
+Contains:
+  1. CNN + BiGRU temporal classifier (MobileNetV2 backbone + Bidirectional GRU)
+  2. YOLO detector loader (Ultralytics wrapper)
+  3. TFLite model loader for edge inference
+
+Usage:
+  from model import build_cnn_bigru, load_detector, load_tflite_model
+"""
+
+import os
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras import layers, models
+
+
+# ─────────────────────────────────────────────────────────────
+# 1. CNN + BiGRU Temporal Model
+# ─────────────────────────────────────────────────────────────
+
+def build_cnn_bigru(
+    seq_length: int = 8,
+    input_size: int = 224,
+    num_classes: int = 2,
+    projection_dim: int = 256,
+    gru_units: int = 128,
+    dropout: float = 0.4,
+    pretrained: bool = True,
+):
+    """Build a CNN (MobileNetV2) + Bidirectional GRU temporal classifier.
+
+    Input shape : (batch, seq_length, H, W, 3)
+    Output      : softmax over `num_classes`
+
+    Args:
+        seq_length:     Number of frames per sequence.
+        input_size:     Height and width each frame is resized to.
+        num_classes:    Number of output classes.
+        projection_dim: Dense projection after CNN features.
+        gru_units:      Hidden units in each GRU direction.
+        dropout:        Dropout rate before final classifier.
+        pretrained:     Use ImageNet weights for MobileNetV2.
+
+    Returns:
+        tf.keras.Model
+    """
+    input_shape = (seq_length, input_size, input_size, 3)
+    inp = layers.Input(shape=input_shape, name="video_sequence")
+
+    # TimeDistributed MobileNetV2 backbone (no classification head)
+    base_cnn = tf.keras.applications.MobileNetV2(
+        input_shape=(input_size, input_size, 3),
+        include_top=False,
+        weights="imagenet" if pretrained else None,
+        pooling="avg",
+    )
+    base_cnn.trainable = True
+
+    td = layers.TimeDistributed(base_cnn, name="td_backbone")(inp)
+    # td shape: (batch, seq_length, features)
+
+    # Optional dense projection per time-step
+    proj = layers.TimeDistributed(
+        layers.Dense(projection_dim, activation="relu"), name="td_proj"
+    )(td)
+
+    # Bidirectional GRU over temporal axis
+    x = layers.Bidirectional(
+        layers.GRU(gru_units, return_sequences=False, reset_after=True),
+        name="bigru",
+    )(proj)
+    x = layers.Dropout(dropout)(x)
+    out = layers.Dense(num_classes, activation="softmax", name="classifier")(x)
+
+    model = models.Model(inputs=inp, outputs=out, name="cnn_bigru")
+    return model
+
+
+# ─────────────────────────────────────────────────────────────
+# 2. YOLO Detector (Ultralytics)
+# ─────────────────────────────────────────────────────────────
+
+def load_detector(model_path: str = "yolov8n.pt"):
+    """Load an Ultralytics YOLO detector.
+
+    Args:
+        model_path: Path to a YOLO checkpoint (.pt).
+
+    Returns:
+        ultralytics.YOLO model instance.
+    """
+    from ultralytics import YOLO
+    return YOLO(model_path)
+
+
+# ─────────────────────────────────────────────────────────────
+# 3. TFLite Model Loader
+# ─────────────────────────────────────────────────────────────
+
+def load_tflite_model(tflite_path: str, num_threads: int = 4):
+    """Load a TFLite model and return an interpreter ready for inference.
+
+    Args:
+        tflite_path: Path to `.tflite` file.
+        num_threads:  CPU threads for the interpreter.
+
+    Returns:
+        tf.lite.Interpreter (already allocated tensors).
+    """
+    interpreter = tf.lite.Interpreter(
+        model_path=tflite_path, num_threads=num_threads
+    )
+    interpreter.allocate_tensors()
+    return interpreter
+
+
+def tflite_predict(interpreter, input_array: np.ndarray) -> np.ndarray:
+    """Run a single forward pass through a TFLite interpreter.
+
+    Args:
+        interpreter:  An allocated tf.lite.Interpreter.
+        input_array:  NumPy array matching the model's input shape & dtype.
+
+    Returns:
+        NumPy array of model outputs.
+    """
+    input_details = interpreter.get_input_details()
+    output_details = interpreter.get_output_details()
+    interpreter.set_tensor(input_details[0]["index"], input_array)
+    interpreter.invoke()
+    return interpreter.get_tensor(output_details[0]["index"])
+
+
+# ─────────────────────────────────────────────────────────────
+# 4. Helper: Mask → YOLO Bounding Boxes
+# ─────────────────────────────────────────────────────────────
+
+def masks_to_bboxes(mask_path: str, min_area: int = 100):
+    """Convert a binary mask image to YOLO-format bounding boxes.
+
+    Args:
+        mask_path: Path to a grayscale mask image.
+        min_area:  Minimum contour bounding-box area to keep.
+
+    Returns:
+        List of tuples (class_id, xc, yc, w, h) in normalised coords.
+    """
+    import cv2
+
+    m = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
+    if m is None:
+        return []
+    thr = (m > 0).astype("uint8") * 255
+    contours, _ = cv2.findContours(thr, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    boxes = []
+    h, w = m.shape[:2]
+    for c in contours:
+        x, y, ww, hh = cv2.boundingRect(c)
+        if ww * hh < min_area:
+            continue
+        xc = (x + ww / 2.0) / w
+        yc = (y + hh / 2.0) / h
+        nw = ww / float(w)
+        nh = hh / float(h)
+        boxes.append((0, xc, yc, nw, nh))
+    return boxes
+
+
+# ─────────────────────────────────────────────────────────────
+# Quick test
+# ─────────────────────────────────────────────────────────────
+
+if __name__ == "__main__":
+    print("Building CNN-BiGRU model …")
+    m = build_cnn_bigru()
+    m.summary()

arm-model/requirements.txt

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+# Core
+ultralytics>=8.0.0
+tensorflow==2.19.1
+opencv-python
+numpy
+pyyaml
+matplotlib
+scikit-learn
+
+# For conversion & benchmarking
+onnx
+onnxruntime

arm-model/run.py

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+"""
+run.py — Unified inference, benchmarking & utility pipeline for Road Anomaly Detection.
+
+Sub-commands:
+  detect      Run YOLO + CNN-BiGRU temporal inference on a video
+  benchmark   Measure detector FPS / latency on a video
+  check-mask  Inspect a mask image (shape, nonzero pixels, unique values)
+  compute-boxes  Extract YOLO bounding boxes from a mask image
+
+Examples:
+  python run.py detect       --video sample_video.mp4
+  python run.py benchmark    --video sample_video.mp4
+  python run.py check-mask   --mask path/to/mask.png
+  python run.py compute-boxes --mask path/to/mask.png --min_area 200
+"""
+
+import argparse
+import os
+import time
+from collections import deque
+
+import cv2
+import numpy as np
+import yaml
+import tensorflow as tf
+
+from model import masks_to_bboxes
+
+
+# ═══════════════════════════════════════════════════════════════
+# Helper — IoU
+# ═══════════════════════════════════════════════════════════════
+
+def _iou(boxA, boxB):
+    """Compute IoU between two [x1, y1, x2, y2] boxes."""
+    xA = max(boxA[0], boxB[0])
+    yA = max(boxA[1], boxB[1])
+    xB = min(boxA[2], boxB[2])
+    yB = min(boxA[3], boxB[3])
+    inter = max(0, xB - xA) * max(0, yB - yA)
+    areaA = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
+    areaB = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])
+    uni = areaA + areaB - inter
+    return inter / uni if uni > 0 else 0
+
+
+# ═══════════════════════════════════════════════════════════════
+# Simple Track
+# ═══════════════════════════════════════════════════════════════
+
+class _Track:
+    """Lightweight track to accumulate cropped frames per detected object."""
+
+    def __init__(self, tid, bbox, seq_len):
+        self.tid = tid
+        self.bbox = bbox
+        self.last_seen = 0
+        self.frames = deque(maxlen=seq_len)
+
+    def add(self, frame, bbox):
+        self.bbox = bbox
+        self.last_seen = 0
+        self.frames.append(frame)
+
+
+# ═══════════════════════════════════════════════════════════════
+# 1. Detect — YOLO + Temporal
+# ═══════════════════════════════════════════════════════════════
+
+class _DetectorTemporal:
+    """Combines YOLOv8 per-frame detection with a CNN-BiGRU temporal classifier."""
+
+    def __init__(self, cfg: dict):
+        from ultralytics import YOLO
+
+        self.cfg = cfg
+        self.detector = YOLO(cfg["detector"]["model"])
+        self.temporal = tf.keras.models.load_model("saved_models/bigru.h5")
+        self.seq_len = cfg["temporal"]["seq_length"]
+        self.input_size = cfg["temporal"]["input_size"]
+        self.tracks: dict[int, _Track] = {}
+        self.next_id = 1
+
+    def process_video(self, video_path: str, out_dir: str = "outputs"):
+        os.makedirs(out_dir, exist_ok=True)
+        cap = cv2.VideoCapture(video_path)
+        fps = cap.get(cv2.CAP_PROP_FPS) or 25
+        frame_idx = 0
+
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            results = self.detector(
+                frame, imgsz=self.cfg["detector"]["input_size"]
+            )[0]
+
+            boxes = []
+            for r in results.boxes:
+                x1, y1, x2, y2 = map(int, r.xyxy[0].tolist())
+                conf = float(r.conf[0])
+                if conf < self.cfg["detector"]["conf_thresh"]:
+                    continue
+                boxes.append((x1, y1, x2, y2, conf))
+
+            # Simple IoU-based association
+            for b in boxes:
+                best_iou = 0
+                best_tid = None
+                for tid, track in self.tracks.items():
+                    i = _iou(b[:4], track.bbox)
+                    if i > best_iou and i > 0.3:
+                        best_iou = i
+                        best_tid = tid
+
+                if best_tid is None:
+                    tid = self.next_id
+                    self.next_id += 1
+                    self.tracks[tid] = _Track(tid, b[:4], self.seq_len)
+                    track = self.tracks[tid]
+                else:
+                    track = self.tracks[best_tid]
+
+                crop = frame[b[1] : b[3], b[0] : b[2]]
+                if crop.size == 0:
+                    continue
+                crop = cv2.resize(crop, (self.input_size, self.input_size))
+                crop = cv2.cvtColor(crop, cv2.COLOR_BGR2RGB)
+                track.add(crop, b[:4])
+
+                # Temporal classification when buffer is full
+                if len(track.frames) >= self.seq_len:
+                    arr = np.array(track.frames).astype("float32") / 255.0
+                    arr = np.expand_dims(arr, 0)
+                    preds = self.temporal.predict(arr)
+                    cls = int(np.argmax(preds, axis=-1)[0])
+                    score = float(np.max(preds))
+                    if cls == 1 and score > self.cfg["temporal"]["threshold"]:
+                        ts = frame_idx / fps
+                        print(
+                            f"Anomaly detected (track {track.tid}) "
+                            f"at {ts:.2f}s  score={score:.3f}"
+                        )
+                        cv2.imwrite(
+                            os.path.join(
+                                out_dir,
+                                f"anomaly_{track.tid}_{frame_idx}.jpg",
+                            ),
+                            frame,
+                        )
+
+            frame_idx += 1
+
+        cap.release()
+        print(f"Processed {frame_idx} frames from {video_path}")
+
+
+def detect(args):
+    """Run full YOLO + temporal inference on a video."""
+    cfg = yaml.safe_load(open(args.config))
+    pipeline = _DetectorTemporal(cfg)
+    pipeline.process_video(args.video, out_dir=args.out_dir)
+
+
+# ═══════════════════════════════════════════════════════════════
+# 2. Benchmark
+# ═══════════════════════════════════════════════════════════════
+
+def benchmark(args):
+    """Measure detector FPS / latency on the first N frames of a video."""
+    from ultralytics import YOLO
+
+    cfg = yaml.safe_load(open(args.config))
+    detector = YOLO(cfg["detector"]["model"])
+
+    cap = cv2.VideoCapture(args.video)
+    times = []
+    frames = 0
+    t0 = time.time()
+
+    while frames < args.max_frames:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        t1 = time.time()
+        detector(frame, imgsz=cfg["detector"]["input_size"])
+        t2 = time.time()
+        times.append(t2 - t1)
+        frames += 1
+
+    cap.release()
+    t_total = time.time() - t0
+    print(f"Processed {frames} frames in {t_total:.2f}s → {frames / t_total:.2f} FPS")
+    print(
+        f"Avg detector inference: {np.mean(times):.3f}s "
+        f"(std {np.std(times):.3f}s)"
+    )
+
+
+# ═══════════════════════════════════════════════════════════════
+# 3. Check Mask
+# ═══════════════════════════════════════════════════════════════
+
+def check_mask(args):
+    """Print basic statistics of a mask image."""
+    img = cv2.imread(args.mask, cv2.IMREAD_GRAYSCALE)
+    if img is None:
+        print("MISSING:", args.mask)
+        return
+    h, w = img.shape
+    nz = int((img > 0).sum())
+    vals = np.unique(img)
+    print(args.mask)
+    print("shape", h, w)
+    print("nonzero", nz)
+    print("unique_vals_sample", vals[:20].tolist())
+
+
+# ═══════════════════════════════════════════════════════════════
+# 4. Compute Boxes from Mask
+# ═══════════════════════════════════════════════════════════════
+
+def compute_boxes(args):
+    """Extract YOLO bounding boxes from a single mask image."""
+    boxes = masks_to_bboxes(args.mask, min_area=args.min_area)
+    print("mask", args.mask)
+    print("min_area", args.min_area)
+    print("boxes_count", len(boxes))
+    for b in boxes:
+        print(b)
+
+
+# ═══════════════════════════════════════════════════════════════
+# CLI
+# ═══════════════════════════════════════════════════════════════
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Road Anomaly Detection — Inference & Utilities"
+    )
+    sub = parser.add_subparsers(dest="command", required=True)
+
+    # ── detect ────────────────────────────────────────────────
+    p_det = sub.add_parser("detect", help="Run YOLO + temporal inference")
+    p_det.add_argument("--video", required=True)
+    p_det.add_argument("--config", default="config.yaml")
+    p_det.add_argument("--out_dir", default="outputs")
+
+    # ── benchmark ─────────────────────────────────────────────
+    p_bench = sub.add_parser("benchmark", help="Measure detector FPS")
+    p_bench.add_argument("--video", required=True)
+    p_bench.add_argument("--config", default="config.yaml")
+    p_bench.add_argument("--max_frames", type=int, default=200)
+
+    # ── check-mask ────────────────────────────────────────────
+    p_chk = sub.add_parser("check-mask", help="Inspect a mask image")
+    p_chk.add_argument("--mask", required=True)
+
+    # ── compute-boxes ─────────────────────────────────────────
+    p_box = sub.add_parser("compute-boxes", help="Mask → YOLO bounding boxes")
+    p_box.add_argument("--mask", required=True)
+    p_box.add_argument("--min_area", type=int, default=200)
+
+    args = parser.parse_args()
+
+    dispatch = {
+        "detect": detect,
+        "benchmark": benchmark,
+        "check-mask": check_mask,
+        "compute-boxes": compute_boxes,
+    }
+    dispatch[args.command](args)
+
+
+if __name__ == "__main__":
+    main()

arm-model/runs/detect/train/args.yaml

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+task: detect
+mode: train
+model: yolov8n.pt
+data: data/yolov8_data_small.yaml
+epochs: 3
+time: null
+patience: 100
+batch: 16
+imgsz: 320
+save: true
+save_period: -1
+cache: false
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+workers: 8
+project: null
+name: train
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
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+rect: false
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+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: 0.0
+compile: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
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+plots: true
+end2end: null
+source: null
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+visualize: false
+augment: false
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+classes: null
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+embed: null
+show: false
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+save_txt: false
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+save_crop: false
+show_labels: true
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+show_boxes: true
+line_width: null
+format: torchscript
+keras: false
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+int8: false
+dynamic: false
+simplify: true
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+lr0: 0.01
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+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
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+pose: 12.0
+kobj: 1.0
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+angle: 1.0
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+auto_augment: randaugment
+erasing: 0.4
+cfg: null
+tracker: botsort.yaml
+save_dir: /home/pragadeesh/ARM/arm-model/runs/detect/train

arm-model/runs/detect/train/results.csv

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arm-model/runs/detect/train/weights/best.pt

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+oid sha256:4377a0907b288a2860989a85dc88ee6e890b75cd7d19ead04a918050fb8bddb0
+size 6202858

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arm-model/runs/detect/train10/args.yaml

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+task: detect
+mode: train
+model: yolov8n.pt
+data: data/yolov8_data_full.yaml
+epochs: 300
+time: null
+patience: 100
+batch: 16
+imgsz: 320
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+cache: false
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+project: null
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+cfg: null
+tracker: botsort.yaml
+save_dir: /home/pragadeesh/ARM/arm-model/runs/detect/train10

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