Abstract: Aiming at the problem of low detection accuracy caused by false detection and missing detection in underwater image target detection， this paper proposes a lightweight underwater image target detection algorithm based on improved YOLOv8n， aiming to improve the detection accuracy of underwater target images. Firstly， the backbone network in YOLOv8 is replaced by the residual network ResNet10 to enhance the feature extraction capability of the backbone network. Secondly， the large convolution kernel attention mechanism is used to improve the fast feature pyramid module to improve the model’s ability to fuse multi-scale features. Then， the C2f module of the original model is replaced by the generalized efficient layer aggregation network in the latest YOLOv9 algorithm， so that the model can maintain high accuracy while reducing computing costs. Finally， the new loss function Inner-SIoU is used to improve the generalization ability of the model and accelerate the convergence speed of the model. Through experiments， on the URPC2020 underwater image target detection dataset， the improved algorithm mAP50 has reached 86.2%， 2.6 percentage points higher accuracy than the original model. Compared with the advanced YOLOv8s and YOLOv7 tiny detectors， as well as the research work in the same field， the method proposed in this paper has achieved higher detection accuracy.

Key words: underwater target detection, YOLOv8, residual network, attention mechanism, loss function