To address the common issues of false detection, low precision, and weak generalization ability in underwater image target detection algorithms, this paper proposes an underwater image target detection algorithm called AWAF-YOLO. First, the anisotropic downsample (ADown) module is used to enhance the network flexibility so as to obtain multi-scale data features more effectively. Second, wavelet transform convolution (WTConv) module is used to reduce the complexity of the model and greatly reduce the computational burden. Then, the spatial pyramid pooling fast and mixed self-attention and convolution (SPPFAC) module is constructed to improve the multi-scale feature discrimination by integrating attention and cross-channel interaction, thus to focus on key feature information more intelligently, and optimize the feature processing process. Finally, the focused intersection over union loss and metric bounding box shape and scale (FSIoU) loss function is introduced to achieve accurate location of complex targets by coupling dynamic focusing with geometric constraints. Experimental results show that compared with the baseline model, the proposed algorithm has an increase of 2.6 percentage points in mean average precision (mAP@0.50∶0.95) in TrashCan1.0 dataset, and an increase of 1.6 percentage points in mAP@0.50∶0.95 in Aquarium dataset. The proposed algorithm has good effectiveness and generalization, provides a new idea for underwater image target detection.