ORIGINAL RESEARCH article
Front. Neurorobot.
Volume 19 - 2025 | doi: 10.3389/fnbot.2025.1588565
Context-Aware Enhanced Feature Refinement for Small Object Detection with Deformable DETR
Provisionally accepted
- 1Advanced Manufacturing Metrology Research Center, Zhejiang Institute of Quality Sciences, Hangzhou, China
- 2Key Laboratory of Acoustics and Vibration Applied Measuring Technology,State Administration for Market Regulation, Hangzhou, China
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Small object detection is a critical task in applications like autonomous driving and ship black smoke detection. While Deformable DETR has advanced small object detection, it faces limitations due to its reliance on CNNs for feature extraction, which restricts global context understanding and results in suboptimal feature representation. Additionally, it struggles with detecting small objects that occupy only a few pixels due to significant size disparities. To overcome these challenges, we propose the Context-Aware Enhanced Feature Refinement Deformable DETR, an improved Deformable DETR network. Our approach introduces Mask Attention in the backbone to improve feature extraction while effectively suppressing irrelevant background information. Furthermore, we propose a Context-Aware Enhanced Feature Refinement Encoder to address the issue of small objects with limited pixel representation. Experimental results demonstrate that our method outperforms the baseline, achieving a 2.1% improvement in mAP.
Keywords: Small object detection, Deformable DETR, mask attention, autonomouts driving, feature extraction
Received: 06 Mar 2025; Accepted: 16 May 2025.
Copyright: © 2025 Shi, Shao, Zhao, Feng, Luo, Ouyang and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jianwen Shao, Key Laboratory of Acoustics and Vibration Applied Measuring Technology,State Administration for Market Regulation, Hangzhou, China
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