YOLO-MBS: a lightweight ship target detection method

In the task of ship target detection, due to the complex image background and more irrelevant interference, the difficulty of ship target detection is increased. A lightweight and efficient ship object detection algorithm YOLO-MBS based on improved Yolov5 is proposed. Firstly, the backbone network adopts MobileNetv3, which uses lightweight deep separable convolution and residual block structures and a new activation function h-swish to effectively reduce the number of parameters of the network model and improve the calculation speed. Then, the E-IOU loss function is introduced to improve the regression accuracy and convergence speed of the prediction box. Secondly, in order to allow the machine to select the region of interest and extract effective features more fully, the SimAM non-parametric attention mechanism is added. Finally, the BiFPN feature pyramid network structure is used to make the features of different scales better integrated and utilized. The experimental results regarding the Seaships dataset indicate that the volume of the improved algorithm model is compressed to 21.8% of the original YOLOv5 model, the training time is shortened by 46.62 %, the parameter quantity is compressed to 18.5 % of the original model, the calculation amount is reduced to 16.45 % of the original model, and the average accuracy is improved to 98.8 %. The experimental results demonstrate that the improved algorithm significantly reduces model parameters, thereby shortening both training and inference times, while maintaining high-precision detection performance.