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A real-time detection algorithm based on the improved YOLOv7 (You Only Look Once v7) was proposed to address the issues of slow detection speed, low accuracy, and easy omission of small targets in traffic sign detection algorithms for intelligent transportation and autonomous driving. The algorithm adopted K-means++ algorithm to cluster initial anchor box sizes of the traffic sign target and used Ghost Net network to replace the backbone feature extraction network of the original YOLOv7 algorithm, reducing the network's parameter size. Additionally, the Convolutional Block Attention Module (CBAM) was introduced in the feature fusion network to enhance the network's sensitivity to key features of traffic signs. Furthermore, a small target detection layer with a smaller receptive field was added to improve the accuracy of detecting small target traffic signs. The SIoU (Scylla-Intersection over Union) loss function was introduced to accelerate the model's convergence. Experimental results showed that the improved YOLOv7 algorithm achieved an accuracy of 96%, a recall rate of 96%, and an average precision of 97%. Compared to the original model, there was an improvement in all metrics. In comparison to YOLOv7, the parameter size and computational cost were decreased by 39% and 32%, respectively. Detection speed was 112.72 frames per second. It can enable real-time and accurate traffic sign detection.
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