DSW-YOLOv7: small target detection algorithm for water surface in complex scenes

Currently, target detection technology is becoming mature, but detecting small targets remains a challenging area of research. To address the issues of small targets on water surfaces, which have less feature information, small coverage areas, and are more prone to occlusion and missed detections, this paper introduces a water surface small target detection model named DSW-YOLOv7. First, the backbone network incorporates deformable convolutions with multiple groups and shared convolutional weights to overcome the shortcomings of insufficient sampling in fixed rectangular structures, expanding the receptive field and enhancing the ability of the model to focus on targets of different sizes. Subsequently, the SimAM attention module is used to enhance network responsiveness to smaller targets while reducing noise effects. Finally, the WIoU v3 loss function is employed, incorporating a dynamic focusing mechanism to boost both the convergence speed and the regression precision of the model. Experimental comparisons are conducted on the FloW-Img sub-dataset publicly released by Orcauboat. The results indicate that the mAP50 of the DSW-YOLOv7 network model can reach 86.5%, which is a 6.4% enhancement relative to the baseline model. Moreover, the detection speed is increased by 19.8%. For images depicting small targets, ultra-small targets, and densely concentrated scenarios, the DSW-YOLOv7 network model shows significant improvement in terms of false positives and missed detection compared to the original network.