| # Traffic Sign Detection Model | |
| This repository contains a deep learning model for **traffic sign detection**. The model is trained to detect and classify traffic signs in real-time, suitable for applications like autonomous driving and advanced driver assistance systems (ADAS). | |
| --- | |
| ## It suitable for : | |
| - **Computer Vision Learning** | |
| - **Academic and research projects** | |
| - **Autonomous driving prototypes** | |
| --- | |
| ## Model Details | |
| - **Model type:** YOLO / PyTorch | |
| - **Task:** Object detection (traffic signs) | |
| - **Dataset:** German Traffic Sign Recognition Benchmarks | |
| - **Input:** Images (RGB) | |
| - **Output:** Bounding boxes + class labels | |
| - **Framework:** PyTorch | |
| - **License:** MIT | |
| --- | |
| ## Dataset | |
| The model was train on German Traffic Sign Recognition Benchmarks (GTSRB) that contain 43 class include with all the images and the all the label. And we filter some sign on the GTSRB and the overall of the filtering contain 33 class. There are 'TurnRightAhead', 'NoPassingTrucks', 'DangerousCurveLeft', 'Yield', 'SpeedLimit60' 'EndNoPassingByTrucks', 'EndNoPassing', 'RoundaboutMandatory', 'KeepLeft', 'NoPassing','KeepRight', 'RightOfWayCrossing', 'PriorityRoad', 'GoStraightOrLeft', 'Stop', 'NoVehicles', 'VehiclesOver3.5TonsProhibited', 'NoEntry', 'GeneralCaution','GoStraightOrRight', 'DangerousCurveRight', 'DoubleCurve', 'BumpyRoad', 'SlipperyRoad', | |
| 'RoadNarrowRight', 'RoadWork', 'TrafficSignals', 'Pedestrians', 'ChildrenCrossing','BicyclesCrossing', 'AheadOnly', 'WildAnimals', 'TurnLeftAhead' | |
| --- | |
| ## Repository Structure | |
| traffic_sign_model/ | |
| ├── model.pt # Model training | |
| ├── config.yaml # Model configuration | |
| ├── README.md # Documentation | |
| --- | |
| ## Install dependencies | |
| ```bash | |
| python -m venv venv | |
| venv\Scripts\activate | |
| source venv/bin/activate | |
| pip install ultralytics | |
| pip install opencv-python | |
| pip install huggingface_hub | |
| ``` | |
| --- | |
| ## Loading the model | |
| ```bash | |
| model = YOLO("yolov8m.pt") | |
| results = model.train( | |
| data="/kaggle/input/traffic-sign-detection/dataset/data.yaml", | |
| epochs=30, | |
| imgsz=1280, | |
| batch=10, | |
| mixup=0.1, | |
| copy_paste=0.15, | |
| amp=True, | |
| workers=2, | |
| patience=15, | |
| name="yolov8m_traffic_1280" | |
| ) | |
| ``` | |
| ## Image Detection | |
| ```bash | |
| import cv2 | |
| from ultralytics import YOLO | |
| model = YOLO("./traffic_sign_model/best_yolov8m.pt") | |
| image = cv2.imread("./image.png") | |
| results = model(image) | |
| annotated_frame = results[0].plot() | |
| cv2.imshow("YOLOv8 Detection Results", annotated_frame) | |
| cv2.waitKey(0) | |
| cv2.destroyAllWindows() | |
| ``` | |
| ## Real-Time Camera Detection | |
| ```bash | |
| from ultralytics import YOLO | |
| import cv2 | |
| model = YOLO("./traffic_sign_model/best_yolov8m.pt") | |
| cap = cv2.VideoCapture(0) | |
| if not cap.isOpened(): | |
| print("Cannot open the webcame") | |
| exit() | |
| while True: | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| results = model.predict(source=frame, conf=0.6) | |
| annotated_frame = results[0].plot() | |
| cv2.imshow("YOLO webcame test", annotated_frame) | |
| if cv2.waitKey(1) & 0xFF == ord('q'): | |
| break | |
| cap.release() | |
| cv2.destroyAllWindows() | |
| ``` | |
| --- | |
| ## References | |
| [1] Ultralytics, "YOLO Documentation," [Online]. Available: https://docs.ultralytics.com/. [Accessed: Dec. 16, 2025]. | |
| [2] R. Kumar, A. Gupta, and D. Rajeswari, "Traffic Sign Detection Using YOLOv8," TIUTIC Journal, vol. 7, Art. no. 10, 2019. [Online]. Available: https://tiutic.org/pdf/volume/Vol_7/Vol7_Article_10.pdf | |
| [3] M. Serna and A. Ruichek, "Traffic Signs Classification by Deep Learning for Advanced Driving Assistance Systems," 2019. [Online]. Available: https://www.researchgate.net/publication/335006038 | |
| [4] Y. Wu, Y. Tian, and J. Liu, "Traffic Sign Detection Based on Convolutional Neural Networks," 2013. [Online]. Available: https://xlhu.cn/papers/Wu13.pdf | |
| [5] A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao, "The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale," in Proc. European Conf. on Computer Vision (ECCV), 2020. [Online]. Available: https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123680069.pdf | |
| [6] Kaggle, "German Traffic Sign Recognition Benchmark (GTSRB) Dataset," 2019. [Online]. Available: https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic | |