This paper proposes an underwater object detection algorithm based on lightweight structure optimization to address the low detection accuracy and difficult deployment in underwater robot dynamic inspection caused by low light, blurriness, and low contrast. The algorithm builds upon YOLOv7 by incorporating the attention mechanism of the convolutional module into the backbone network to enhance feature extraction in low light and blurred environments. Furthermore, the feature fusion enhancement module is optimized to control the shortest and longest gradient paths for fusion, improving the feature fusion ability while reducing network complexity and size. The output module of the network is also optimized to improve convergence speed and detection accuracy for underwater fuzzy objects. Experimental verification using real low-light underwater images demonstrates that the optimized network improves the object detection accuracy (mAP) by 11.7%, the detection rate by 2.9%, and the recall rate by 15.7%. Moreover, it reduces the model size by 20.2 MB with a compression ratio of 27%, making it more suitable for deployment in underwater robot applications.