Research and application based on the improved YOLO V7 target detection algorithm

At present, YOLO-based of algorithms have been widely used in urban planning, traffic monitoring, ecological protection, military security and other fields, and their applicable scenarios are expanding. In view of the problems of high misdetection rate, high omission rate and insufficient accuracy in the image target detection task, this topic is dedicated to study the improved target detection algorithm based on YOLO V7. In order to optimize the time cost and computing resource consumption, the Anchor-free based design is introduced, and through optimizing the design of decoupling head, the independent processing of classification and regression tasks is realized to improve the efficiency of feature extraction. Based on this method, the CBATM attention mechanism is used to better capture the intercorrelations between features and improve the representational ability of the model. In the loss function section, this paper adopts the SimOTA method in YOLOX to realize the dynamic number allocation of positive samples, which greatly reduces the training time. Improved YOLO V7 target detection algorithm in the PASCAL VOC challenge public dataset VOC2007 data set, the results show that the improved YOLO V7 target detection algorithm than the original YOLO V7, has higher detection accuracy and efficient performance, the average detection accuracy (mAP) increased by 2.27%, compared with other classic target detection algorithm, the improved YOLO V7 performance is more accurate and efficient.