SO-DETR: Leveraging Dual-Domain Features and Knowledge Distillation for Small Object Detection

• URL: http://arxiv.org/abs/2504.11470v1
• Date: Fri, 11 Apr 2025 13:47:37 GMT
• Title: SO-DETR: Leveraging Dual-Domain Features and Knowledge Distillation for Small Object Detection
• Authors: Huaxiang Zhang, Hao Zhang, Aoran Mei, Zhongxue Gan, Guo-Niu Zhu,
• Abstract summary: This paper proposes an efficient model, Small Object Detection Transformer (SO-DETR)<n>The model comprises three key components: a dual-domain hybrid encoder, an enhanced query selection mechanism, and a knowledge distillation strategy.<n> Experimental results on the VisDrone 2019-DET and UAVVaste datasets demonstrate that SO-DETR outperforms existing methods with similar computational demands.
• Score: 15.03203094818889
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detection Transformer-based methods have achieved significant advancements in general object detection. However, challenges remain in effectively detecting small objects. One key difficulty is that existing encoders struggle to efficiently fuse low-level features. Additionally, the query selection strategies are not effectively tailored for small objects. To address these challenges, this paper proposes an efficient model, Small Object Detection Transformer (SO-DETR). The model comprises three key components: a dual-domain hybrid encoder, an enhanced query selection mechanism, and a knowledge distillation strategy. The dual-domain hybrid encoder integrates spatial and frequency domains to fuse multi-scale features effectively. This approach enhances the representation of high-resolution features while maintaining relatively low computational overhead. The enhanced query selection mechanism optimizes query initialization by dynamically selecting high-scoring anchor boxes using expanded IoU, thereby improving the allocation of query resources. Furthermore, by incorporating a lightweight backbone network and implementing a knowledge distillation strategy, we develop an efficient detector for small objects. Experimental results on the VisDrone-2019-DET and UAVVaste datasets demonstrate that SO-DETR outperforms existing methods with similar computational demands. The project page is available at https://github.com/ValiantDiligent/SO_DETR.

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