MFLM-FPN and GAFF-driven Underwater Target Detection Algorithms and Class Balancing Strategies

To address the problem of scarce feature information for underwater targets, this paper proposes a feature pyramid mapping mechanism combined with global attention. This mechanism maps each proposal box to different feature layers, resulting in four feature layers of consistent size and complementary information after region-of-interest pooling. Global attention is then used to achieve feature fusion, fully utilizing the feature information from each layer and effectively alleviating the problem of feature scarcity for underwater targets. To address the class imbalance problem in underwater datasets, a copy-paste class balancing strategy is designed to enhance the neural network's attention to scarce categories such as sea cucumbers, starfish, and scallops. To address the issue of insufficient penalty in the loss function leading to decreased detection accuracy, the normalized distance between the predicted and target boxes is introduced as a penalty term in the smoothed L1 loss function, significantly improving the localization accuracy of underwater multi-scale targets. Experimental results show that on the National Underwater Robotics Competition dataset, the proposed method achieves a recognition accuracy of 81.93%, a 5.71% improvement over the baseline model Faster R-CNN, effectively reducing false negatives and false positives in complex underwater environments.