P‐2.5: Research on real‐time performance of Road condition display for automatic driving

Abstract

In view of the problems of multi‐scale, densely distributed and difficult small target detection in real‐time road condition detection, a new improved yolov5 real‐time target detection method was proposed. The activation function in the original spatial pyramid (SPPF) was replaced by ReLu in the backbone network to enhance the expression ability of the model. The bidirectional feature pyramid (BiFPN) is used to replace the original feature pyramid network (FPN) + pixel aggregation network (PAN) in the neck area. The bidirectional feature fusion improves the utilization of multi‐scale semantic features and strengthens the extraction of image deep features. The convolutional attention mechanism (CBAM) is introduced to further improve the feature extraction ability of the algorithm and make the algorithm pay more attention to useful information. The results of self‐made traffic condition data set show that the average accuracy of the improved yolov5 model reaches 81.3%, which is 10.9% higher than that of the original yolov5 model, showing better detection accuracy and real‐time performance. Compared with some mainstream target detection algorithms, this algorithm has certain advantages.