BS-YOLO:基于BSAM注意力机制和SCConv的小目标检测算法OA

In recent years,there has been significant progress in terms of accuracy and robustness of deep-learning-based algorithms for object detection that have been widely applied in industry.However,in the field of small object detection,currently used object detection algorithms suffer from high rates of missed detections and false positives.Therefore,in this study,a YOLO small object detection algorithm,viz.,BS-YOLO,which is based on SCConv and BSAM attention mechanism,is developed.First,in response to the problem of the large amount of redundant information generated in the feature extraction network,a new module,viz.,C3SC,is proposed to reconstruct the backbone network using SCConv.This module reduces redundant information in both spatial and channel aspects of the extracted feature maps,thereby improving the quality of the feature maps extracted by the backbone network,and in turn enhancing detection accuracy.Second,a new attention mechanism,viz.,BSAM,is proposed by combining CBAM and the BiFormer self-attention mechanism,by which weights are allocated reasonably in both spatial and channel aspects,making the feature map more focused on effective information and suppressing background interference.Finally,to solve the problem of uneven distribution of difficult and easy samples in terms of small object detection,Slideloss is used to optimize the loss function,thereby improving the effectiveness of the algorithm for small object detection.The experimental results obtained using the RSOD dataset show that the BS-YOLO algorithm has a precision of 94.2％,a recall rate of 91.6％,and a mAP@0.5 of 95.9％,corresponding to improvements of 3.3,0.1,and 3.6 percentage,respectively,compared to the original YOLOv5 algorithm.This indicates that the BS-YOLO algorithm can effectively improve the accuracy of small object detection and reduce the missed detection rate.