Comparative experiments on the URPC2020 dataset.

The issues of complex background interference, dense distribution, and insufficient feature representation for small objects have become significant challenges and research hotspots in computer vision. Particularly when the algorithm needs to be deployed in practical applications, many state-of-the-art detectors struggle to balance accuracy and efficiency, often requiring extensive computational power or suffering from degraded detection performance on small objects. To tackle these problems, this paper proposes a lightweight dynamic attention-enhanced DETR (LDA-DETR). Firstly, a lightweight feature extraction backbone (LFEB) is designed to improve the efficiency of object detection under limited computational resources. The proposed backbone enhances gradient flow and reduces the model’s parameters through residual structures and partial convolution operations. Then, a Dynamic Multi-Scale Fusion Module (DMSFM) is proposed to improve the model’s adaptability and the ability to fuse diverse features. The proposed module enhances feature representation ability and inference performance by performing convolutions at different scales across multiple branches and dynamically selecting operations. Finally, considering shallow features contain more detailed information, the Attention-Enhanced Fusion Network (AEFN) is constructed. The proposed approach refines and enriches features through attention mechanisms and cascading operations, endowing the features with comprehensive semantic and spatial details. Extensive experiments on the RSOD, NWPU VHR-10, URPC2020, and VisDrone-DET datasets demonstrate that LDA-DETR outperforms the state-of-the-art detection methods and further validate that the technique is better suited for small object detection applications.