At present, fish culture is moving in the direction of precision culture, and fish target detection is an important part of precision culture. Fortunately, the use of deep learning holds promise for fish target detection. However, the existing fish target detection models have the problems of heavy computation and low accuracy.To address the issues of low accuracy and high computational load in fish target detection,a lightweight fish target detection method based on an improved YOLOv8 model was proposed and named YOLOv8-FCW in this study. Firstly,The experimental comparison of MobileNet, ShuffleNet, GhostNet and C2f-Faster show that C2f-Faster has the best performance.Therefore,the FasterBlock from FasterNet was introduced to replace the Bottleneck module in C2f of YOLOv8, reducing redundant computations in the network model. Secondly, the Convolutional Block Attention Module (CBAM) attention mechanism was incorporated to efficiently extract fish body features and enhance the detection accuracy of the network model. Finally, The experimental results show that the loss value and convergence speed of Wise intersection over union (WIoU) loss function are better than Complete intersection over union (CIoU), Distance intersection over union (DIoU) and Generalized Intersection over Union (GIoU).Therefore,a dynamic non-monotonic focusing mechanism WIoU was introduced to replace CIoU, accelerating the convergence speed of the network model and improving its detection performance.In order to verify the detection effect of YOLOv8-FCW on fish, the original model and YOLOv8-FCW were trained and tested on the fish data set.The fish data set consists of 1000 images, which were divided into training set, verification[] set and test set according to the ratio of 8:1:1. Experimental results show that compared with the original model, the improved YOLOv8-FCW model had increased precision by 1.6 percentage points, recall by 5.1 percentage points, and mean average precision(mAP) mAP0.5 metrics by 2.4 percentage points, while the weight and computational load were reduced to 80% and 79% of the original model, respectively.YOLOv8-FCW achieves high detection accuracy and efficiency with very small model volume and low computational cost. The model shows high accuracy and robustness. The research can help breeders accurately calculate the number of fish and provide technical reference for fish target detection.