Automatic underwater object detection methods play an important role in intelligent marine fishing. To address the problem that the existing object detection methods are not accurate enough for underwater biological detection, this study proposes an underwater object detection method based on the GA-RetinaNet algorithm. Firstly, according to the existence of dense objects in underwater images, the study introduces group convolution to replace ordinary convolution, which can provide more feature information without increasing the complexity of parameters and thereby improve the accuracy of the model. Secondly, according to the characteristic that underwater objects are mostly small objects, the attention-guided context feature pyramid network (AC-FPN) is introduced. The context extraction module is used to obtain more receptive fields while guaranteeing high-resolution inputs and thus extract more contextual information. The context attention module and the content attention module are utilized to capture useful features for the accurate positioning of the object. Experimental results show that the improved GA-RetinaNet algorithm enhances the detection accuracy by 2.3% compared with the original algorithm when the URPC2021 dataset is selected. Compared with other mainstream models, the GA-RetinaNet algorithm achieves better detection results for different types of underwater objects, and the detection accuracy is greatly improved.