The research on underwater object detection algorithms is a prerequisite for achieving intelligent fishing with underwater robots. The problems of fuzzy object， numerous small objects and mutual occlusion in underwater object detection pose challenges to the realization of accurate object detection. This paper proposes a YOLO-U algorithm for underwater object detection based on YOLOv7-tiny. The algorithm introduces a RepViT backbone network with structural reparameterization and fuses an ESE channel attention mechanism to enhance the feature extraction capability for underwater fuzzy objects. Additionally， a feature pyramid network CAFPN with shallow coordinate information feature fusion is designed to further enhance the sensitivity of the detection model to directional and positional information， and integrate feature information of different scales to improve the detection ability of small objects. Furthermore， the WIoUv2 bounding box loss function is employed to effectively reduce the contribution of easy examples to the loss value. This allows the model to focus on occluded objects and further improve the detection accuracy for occluded objects. The YOLO-U algorithm achieves a mAP50 of 84.6% on the URPC2021 dataset， which is an improvement of 2.1%， 5.2%， and 2.8% compared to YOLOv7-tiny， YOLOv5s， and YOLOv8s， respectively. The detection results show that the algorithm can effectively improve the detection accuracy of underwater objects and further improve the detection performance of underwater fuzzy objects， small objects， and occluded objects.