Abstract: As one of the research fields of machine vision, ship target detection has fundamental practical significance for marine transportation industry and intelligent search and rescue. However, in actual detection, due to the low accuracy and inaccurate positioning in the complex weather environment, this paper proposes a multi-feature fusion ship target detection algorithm in the complex environment. The side fusion path network is introduced, the loss of feature forward propagation is reduced, information fusion is strengthened. By improving the positioning loss function through Gaussian distribution and the use of variance voting method, the effect of filtering duplicate frames is improved, which makes the frame positioning more accurate, and reduces missed detections and false detections. Experiment results show that in different weather environments, the average accuracy rate （mAP） of the algorithm reaches 88.01%, which is 19.70 and 15.13 percentage points higher than the traditional YOLOv3 and Faster RCNN algorithms, and the average intersection ratio （IoU） increases by 6.49 percentage points , it has good practicability in ship inspection applications in complex environments.

Key words: ship target detection, multi-feature fusion, Faster RCNN, side fusion path network, Gaussian distribution