To address the unstable detection of marine targets challenged existing artificial intelligence algorithms due to the target's complex poses and variable scales,a detection approach based on deep supervision and improved YOLOv8 is proposed.A multi-scale convolution module is designed to extract the feature information of the target's multi-receptive fields and reduce the missed detection rate.Then,a deep supervision network is added to improve the utilization ratio of deep class information and shallow location information,thus optimizing the performance of the backbone network in target feature extraction.Finally,a channel attention mechanism is introduced into the detection head to filter the irrelevant information and enhance the recognition rate of key features.Experiments on the marine target dataset show that the mAP value and the recall rate of the proposed approach reach 93.69% and 85.16%,respectively,which are 7.38 and 8.52 percentage points higher than those of the original model,and the proposed approach outperforms both classical and novel algorithms.The detection time is about 14 ms,which meets the requirements of real-time marine target detection and provides technical support for shipping management and marine accident prevention.