Underwater object detection is an important application of underwater vehicles. However, traditional underwater object detection algorithms have several shortcomings in underwater settings. These include imprecise feature extraction, slow detection speeds, and a lack of robustness. To address these shortcomings, a novel approach based on YOLOv5‐MH (Multi‐Head) is proposed in this paper. Firstly, an image enhancement technique is utilised. This technique uses adaptive linear mapping to adjust contrast and improve the quality of underwater images. Secondly, the C2f module for feature extraction is employed to enable more effective capture of object characteristics. Subsequently, the multi‐head self‐attention and coordinate attention are integrated into the network's backbone. This integration increases the attention given to input data, thereby enhancing the network's performance in handling complex tasks. Furthermore, a bidirectional feature pyramid is implemented to adeptly handle objects of varying scales and sizes, and elevate model performance. Finally, through comprehensive testing on the 2018 URPC dataset and deep plastic dataset, this method demonstrates superior performance. This performance is compared to the original YOLOv5 and other similar networks. It holds immense promise for practical applications across a wide spectrum of underwater tasks.