| | --- |
| | title: HRSID Ship Detection Demo |
| | emoji: 🚢 |
| | colorFrom: blue |
| | colorTo: indigo |
| | sdk: gradio |
| | sdk_version: 4.44.0 |
| | app_file: app.py |
| | pinned: false |
| | license: apache-2.0 |
| | tags: |
| | - code |
| | - deeplearning |
| | - torch |
| | - objectDetection/segmentation |
| | --- |
| | |
| | # 🚢 HRSID Ship Detection Demo |
| |
|
| | This Gradio Space demonstrates a Faster R-CNN model trained on HRSID dataset for ship detection in SAR imagery. |
| |
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| | Upload a SAR image to detect ships. |
| |
|
| |
|
| | --- |
| | license: apache-2.0 |
| | tags: |
| | - object-detection |
| | - synthetic-aperture-radar |
| | - detectron2 |
| | - faster-rcnn |
| | - ship-detection |
| | datasets: |
| | - HRSID |
| | metrics: |
| | - mAP |
| | --- |
| |
|
| | # 🚢 Ship Detection in SAR Imagery using Faster R-CNN (HRSID) |
| |
|
| | ## Abstract |
| |
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| | Synthetic Aperture Radar (SAR) imagery enables all-weather, day-and-night maritime monitoring. However, ship detection in SAR images is challenging due to speckle noise, varying vessel scales, and complex coastal backgrounds. |
| |
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| | This work presents a **Faster R-CNN (ResNet-50 FPN)** based object detection model trained on the **HRSID dataset** for robust ship detection in high-resolution SAR imagery. |
| |
|
| | --- |
| |
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| | ## 📷 Qualitative Results |
| |
|
| | ### Detection Output |
| |  |
| |
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| | The model successfully detects ships of varying scales in cluttered maritime environments. |
| |
|
| | --- |
| |
|
| | ## 🧠 Model Architecture |
| |
|
| | - **Framework:** Detectron2 |
| | - **Detector:** Faster R-CNN |
| | - **Backbone:** ResNet-50 + Feature Pyramid Network (FPN) |
| | - **Classes:** 1 (Ship) |
| | - **Inference Device:** CPU compatible |
| |
|
| | --- |
| |
|
| | ## 📊 Training Configuration |
| |
|
| | | Parameter | Value | |
| | |-----------|--------| |
| | | Dataset | HRSID | |
| | | Image Resolution | 1400 × 1400 | |
| | | Iterations | {MAX_ITER} | |
| | | Score Threshold | {SCORE_THRESH} | |
| | | Optimizer | SGD | |
| | | Learning Rate | 0.0025 | |
| |
|
| | --- |
| |
|
| | ## 📈 Evaluation |
| |
|
| | | Metric | Value | |
| | |--------|--------| |
| | | mAP@0.5 | (Add your value) | |
| | | Precision | (Add value) | |
| | | Recall | (Add value) | |
| |
|
| | > Note: Metrics computed on validation split of HRSID dataset. |
| |
|
| | --- |
| |
|
| | ## 🛰 Dataset Description |
| |
|
| | HRSID (High-Resolution SAR Images Dataset) contains: |
| |
|
| | - 5000+ SAR images |
| | - Thousands of annotated ship bounding boxes |
| | - Multi-scale vessel distribution |
| | - Coastal and open-sea scenarios |
| |
|
| | --- |
| |
|
| | ## ⚙ Inference Code |
| |
|
| | ```python |
| | from detectron2.config import get_cfg |
| | from detectron2.engine import DefaultPredictor |
| | import cv2 |
| | |
| | cfg = get_cfg() |
| | cfg.merge_from_file("config.yaml") |
| | cfg.MODEL.WEIGHTS = "model_final.pth" |
| | cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 |
| | |
| | predictor = DefaultPredictor(cfg) |
| | |
| | image = cv2.imread("test_sar.jpg") |
| | outputs = predictor(image) |
| | print(outputs) |
| | |
