Small target detection algorithm in remote sensing images integrating attention and contextual information
When detecting small targets in multi-scale remote sensing images,target detection algorithms based on deep learning are prone to false detection and missed detection.One of the reasons is that the feature extraction module carries out multiple down-sampling operations.The second reason is the failure to pay attention to the contextual information required by different categories and different scales of targets.To solve this problem,a small object detection algorithm in remote sensing images integrating attention and contextual information ACM-YOLO(Attention-Context-Multiscale YOLO)was proposed.Firstly,to reduce the loss of small target feature information,fine-grained query aware sparse attention was applied,thereby avoiding missed detection.Secondly,to pay more attention to the contextual information required by different categories of remote sensing targets,the Local Contextual Enhancement(LCE)function was designed,thereby avoiding false detection.Finally,to strengthen multi-scale feature fusion capability of the feature fusion module on small targets in remote sensing images,the weighted Bi-directional Feature Pyramid Network(BiFPN)was adopted,thereby improving detection effect of the algorithm.Comparison experiments and ablation experiments were performed on DOTA dataset and NWPU VHR-10 dataset to verify effectiveness and generalization of the proposed algorithm.Experimental results show that on the two datasets,the proposed algorithm has the mean Average Precision(mAP)reached 77.33%and 96.12%respectively,and the Recall increases by 10.00 and 7.50 percentage points,respectively,compared with YOLOv5 algorithm.It can be seen that the proposed algorithm improves mAP and recall effectively,which reduces false detection and missed detection.
万方数据
维普
