Efficient-Deformable-DETR: Enhancing underwater small target detection in complex environments

The marine environment is facing multiple challenges, and the development of underwater small target detection technology is crucial for protecting marine ecology. This paper focuses on the problem of small target detection in complex underwater environments and proposes an improved model based on the Deformable-DETR model. By introducing global response normalization and a fully convolutional mask autoencoder framework, the model’s feature aggregation ability and detection accuracy are enhanced. At the same time, a gradient harmonization mechanism is used to solve the problem of positive and negative sample imbalance, which enhances the model’s learning performance. Experimental results show that the improved model achieves a mAP value of 84.5% on the URPC2020 dataset, an increase of 1.7% over the original model. This technical optimization not only improves the accuracy of underwater small target detection but also contributes to marine ecological monitoring, rational utilization of resources, and environmental protection, promoting the realization of green environment and sustainable development goals.