Vehicle target detection method based on improved YOLO V3 network model

For the problem of insufficient small target detection ability of the existing network model, a vehicle target detection method based on the improved YOLO V3 network model is proposed in the article. The improvement of the algorithm model can effectively improve the detection ability of small target vehicles in aerial photography. The optimization and adjustment of the anchor box and the improvement of the network residual module have improved the small target detection effect of the algorithm. Furthermore, the introduction of the rectangular prediction frame with orientation angles into the model of this article can improve the vehicle positioning efficiency of the algorithm, greatly reduce the problem of wrong detection and missed detection of vehicles in the model, and provide ideas for solving related problems. Experiments show that the accuracy rate of the improved algorithm model is 89.3%. Compared to the YOLO V3 algorithm, it is improved by 15.9%. The recall rate is improved by 16%, and the F1 value is also improved by 15.9%, which greatly increased the detection efficiency of aerial vehicles.

Automated summary

This article proposes an improved YOLO V3 network model to address the issue of insufficient small target detection ability in existing network models. The algorithm model improvement effectively enhances the detection ability of small target vehicles in aerial photography. The optimization of anchor boxes, adjustment of the network residual module, and introduction of rectangular prediction frames with orientation angles have improved the algorithm's small target detection effect and vehicle positioning efficiency.