Create models/object_detection.py

models/object_detection.py

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+import cv2
+import numpy as np
+import torch
+
+# Load pretrained YOLO model or Faster R-CNN
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # Using small YOLOv5 for demonstration
+
+def detect_faults(video):
+    """
+    Detect faults like cracks, dirt, etc. in the given video using YOLOv5 model.
+    Args:
+    - video (cv2.VideoCapture): Input video or image file
+
+    Returns:
+    - result (list): List of detected faults with confidence scores
+    """
+    faults = []
+    while True:
+        ret, frame = video.read()
+        if not ret:
+            break
+        results = model(frame)  # Run the model on the frame
+        result = results.pandas().xywh[0]  # Get bounding box and label data
+        faults.append(result)
+    
+    video.release()
+    return faults