基于轻量级网络的小目标检测算法OACSTPCD

In view of the low accuracy of YOLOv5 algorithm in detecting small objects,a lightweight network KOS-YOLOv5 which aims to improve the accuracy of small object detection is proposed.The K-means++ clustering technology is used to select a set of appropriate anchor box sizes as priors for the model,achieving more accurate anchor box sizes for small objects and enabling the model to adapt to objects with different sizes.A simplified positive-negative sample assignment strategy,which refers to SimOTA,is utilized for dynamic sample matching,so as to optimize the loss function more effectively.The spatial context pyramid(SCP)module is integrated into the algorithm's detection layer,which encourages the backbone network to pay more attention to the feature information of small objects,so as to enhance the capability of extracting object features and improve the accuracy of object detection.The results show that the improved KOS-YOLOv5 algorithm improves the detection precision by 4%,the recall rate by 2.4%,the mean average precision by 3.1%and the loss function value by 5%in comparison with those of the traditional YOLOv5 model.The final detection accuracy of the improved algorithm is 95.38%.