Aiming at the low accuracy problem of small target traffic sign detection in real scenes,a method was proposed to improve YOLOv5 for small target traffic sign detection.The original backbone network was simplified,which reduced the com-plexity of the network.The high-resolution feature fusion network was used to reduce the loss of resolution during feature fusion.On the premise of maintaining three-scale detection,the large-size detection head was introduced to improve the detection ability of small targets.The CBAM attention mechanism was introduced,which mined feature information about small objects.To improve the effect of feature learning,SPD-Conv was introduced to replace the strided convolution in the network.Experi-mental results on the TT100K dataset show that the detection accuracy of the proposed method on small target traffic signs is 79.3％,which is 6.1％higher than that of the original YOLOv5 algorithm,and its overall detection effect is better than that of mainstream target detection algorithms such as YOLOX.The detection rate of the algorithm is 39.4 f/s,which meets the requirement of real-time detection.
维普
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