Due to complex image backgrounds, multiscale coexistence and wide distribution of targets in underwater optical image target detection, an underwater target detection algorithm named MEAS-YOLO is proposed here. Firstly, this algorithm augments training samples to achieve data enhancement by utilizing the Mosaic and Mixup algorithms. Secondly, the efficient multi-scale attention module is integrated with the YOLOv5 backbone section to enhance the model′s feature extraction capabilities. Simultaneously, the adaptively spatial feature fusion structure is introduced to enable the model to fully integrate features of different scales. Finally, the SIoU is used in the network model to improve detection accuracy. Experimental results demonstrate that our model has a mAP of 86.4% on the URPC 2020 dataset, improving the mAP by 2.1% than that of original model. This model exhibits high detection accuracy and lower model params, which provides a new support for precise underwater target detection.