Underwater target detection plays an indispensable role in the fields of marine exploration, resource exploitation, and marine protection. However, complex underwater environments, such as insufficient light, turbid water, and target occlusion, place high demands on the accuracy and effectiveness of target detection. To address the above problems, we propose the ESCL-YOLO underwater target detection algorithm based on improved YOLOv8. First, we design an efficient shared detail-enhanced convolutional detection head, which reduces the number of model parameters while improving the performance of the detection head in localization and classification as well as the ability of detail capture. To enhance the ability of model perception and extraction of fine features, space-to-depth convolution is introduced to replace part of the ordinary convolution in YOLOv8. On this basis, the Content-aware Reassembly of Features upsampling operator is introduced to improve the content perception ability of the model while increasing the sensory field of the model. Finally, a large separable kernel attention is introduced into the spatial pyramid pooling layer of the model, which allows the model to accurately capture the feature information of interest at different scales, further improving the accuracy of the model. Through extensive experiments and analysis on the 2020 National Underwater Robotics Professional Competition dataset and the real-world underwater object detection dataset, the precision, recall, and mean average precision (mAP50) of the algorithm reached 84.6%, 76.9%, and 84.2% and 85.9%, 79.9%, and 86.4%, respectively. Compared with the original model, the mean average precision (mAP50) is improved by 1.6% and 1.3%.