Road object detection method based on improved YOLOv5 algorithm
Aiming at the object detection problems of the existing network such as poor remote object recognition effect,insufficient object feature expression and inaccurate object positioning,a road object detection method based on the improved YOLOv5 algorithm was proposed.Firstly,the feature extraction structure of YOLOv5 algorithm was summarized,and the shortcomings of the original network structure were analyzed.Secondly,the small object detection layer was added to the original network,and the recognition ability of the network for distant objects was improved by supplementing the fusion feature layer and introducing additional detection heads.Thirdly,the original detection head was decoupled,and the expression ability of the network to object features was improved by changing the border regression and object classification process to two branches.Then,the prior box was reclustered,and the height to width ratio of the prior box was adjusted by the K-means++algorithm to enhance the network's ability to locate the object.Finally,AP,mAP and FPS were used as evaluation indicators for ablation,comparison and visual verification experiments.The results show that the detection speed of the proposed algorithm on the BDD100K dataset is 95.2 frames per second,and the average accuracy reaches 55.6%,which is 6.7%higher than that of the YOLOv5 algorithm.It can be seen that the improved YOLOv5 algorithm not only meets the requirements of real-time detection,but also has good object detection accuracy,which is suitable for road object detection tasks in complex traffic environments,and has guiding significance for improving the visual perception ability of autonomous vehicles.
万方数据
维普
