Ship Detection Algorithm Based on YOLOv5 Network Improved with Lightweight Convolution and Attention Mechanism

Abstract

Aiming at the problem of insufficient feature extraction, low precision, and recall in sea surface ship detection, a YOLOv5 algorithm based on lightweight convolution and attention mechanism is proposed. We combine the receptive field enhancement module (REF) with the spatial pyramid rapid pooling module to retain richer semantic information and expand the sensory field. The slim-neck module based on a lightweight convolution (GSConv) is added to the neck section, to achieve greater computational cost-effectiveness of the detector. And, to lift the model’s performance and focus on positional information, we added the coordinate attention mechanism. Finally, the loss function CIoU is replaced by SIoU. Experimental results using the seaShips dataset show that compared with the original YOLOv5 algorithm, the improved YOLOv5 algorithm has certain improvements in model evaluation indexes, while the number of parameters in the model does not increase significantly, and the detection speed also meets the requirements of sea surface ship detection.