Aiming at the problem of missed target detection and false alarms in sea surface target detection in complex nearshore environments and partial target occlusion conditions, a sea surface target detection method fusing lidar and machine vision is proposed. Firstly, a feature extraction module based on attention mechanism and deformable convolution is designed to improve the ability of YOLOv7-tiny to extract features of sea surface obstacle targets, thereby reducing the missed detection rate and false alarm rate caused by complex nearshore background interference. Then, the lidar clustering results and the improved YOLOv7-tiny network model prediction results are fused to reduce the missed detection rate caused by partial occlusion of the target. Finally, experimental verification is conducted on the sea surface target detection image data set. The results showed that compared with the original YOLOv7-tiny network model, the mAP of the improved YOLOv7-tiny network model is increased by 3.8 percentage points. Experimental verification is conducted on real ship experimental data in a scene where the target is partially occluded. Compared with the NMS algorithm, the missed detection rate of the proposed fusion method is reduced by 6.9 percentage points, which verifies that this method can reduce the missed detection rate and false alarm rate of sea surface target detection in complex nearshore environments and partially blocked target scenarios.