YOLO-based lightweight traffic sign detection algorithm and Mobile deployment
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Abstract:
The paper proposes the lightweight traffic sign detection system based on Yolo. Firstly, the C2f structure is inte-grated into the backbone network, employing deformable convolution and BiFPN_Concat to improve the adapta-bility of the network. Secondly, the SimAm is embedded to prioritize key features and reduce the complexity of the model after the C2f layer at the end of the backbone network. Next, the Focal EIoU is introduced to adjust the weights of challenging samples. Finally, we accomplish the design and deployment for the mobile app. The results demonstrate improvements, with the F1 score of 0.8987, mAP@0.5 of 98.8%, mAP@0.5:0.95 of 75.6% and the detection speed of 50 FPS.
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