Effectively monitoring marine fish is necessary to protect and utilize marine fish resources. However, the complexity of marine environment leads to low accuracy in marine fish identification and detection. Therefore, this study proposes an improved algorithm for detecting marine fish based on RetinaNet. First, DenseNet-121 was used to replace the original backbone network of RetinaNet, thereby reducing the number of parameters and retaining more fish image features. To guide the neural network to extract image features more pertinently, the convolution attention module was introduced into the backbone network. Second, a new convolution layer was introduced in the original FPN such that the improved PFPN network can fuse more image features with more scales. Finally, soft-NMS was introduced in the classification and regression network to effectively address the detection-missing problem owing to close proximity and mutual occlusion of the same fish species. The experimental results indicate that the average accuracy of the proposed algorithm is 92.12%. This value is significantly improved compared with SSD and other existing algorithms and is 4.71% higher than that of the original algorithm. Thus, the proposed algorithm efficiently identifies and detects marine fish.