Small object detection in remote sensing images based on improved YOLOv5s
Due to the difficulty of accurately detecting small objects in existing object detection models and the existence of a large number of false positives and false negatives,this paper proposes a small object detection algorithm for remote sensing images based on YOLOv5s.Firstly,a SimAM parameter-free attention mechanism module is added to the backbone network to make the algorithm pay more attention to important features without adding additional parameters,while suppressing background information in remote sensing images.Then,SPD-Conv modules are used for down-sampling to avoid the loss of feature information.Subsequently,a four-scale detection feature fusion network is introduced to obtain richer multi-scale feature information and optimize the fusion network and output detection head structure.Finally,accurate detection of small objects in remote sensing images is achieved.The experimental results on the self-made LEVIR-4SC remote sensing image small object dataset show that compared with YOLOv5s,the model parameters of the proposed algorithm are reduced by 6.3％,and the accuracy(P)and average accuracy mean(mAP)reach 90.7％ and 83.7％,which are improved by 6.5％ and 4.2％.Compared with classic methods such as YOLOv8,the proposed algorithm has fewer parameters and a lighter model,which improves both accuracy and average accuracy,proving that the proposed algorithm has better detection performance.
万方数据
维普
