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<p>Low-light object detection frequently suffers from weak contrast, high noise, and poor performance on small objects. Existing methods often adopt image enhancement to improve detection performance, which results in substantial computational resource consumption. To address these challenges, we propose TinyDark-YOLO, an adaptive and efficient low-light object detection algorithm based on YOLO11. First, we design an Adaptive Gamma Enhancement (AGE) module to adaptively adjust the brightness of input images. Second, we introduce an Attention-based Intra-scale Feature Interaction (AIFI) module to construct global context modeling on high-level features and capture long-range spatial dependencies, improving the detection accuracy of small objects. Finally, we propose a Lite Efficient Head (LEHead) to reduce computational overhead while enhancing the model’s ability to capture weak features in dark environments. We conduct validation and ablation experiments on the ExDark dataset. The results demonstrate that the proposed model achieves an mAP@0.5 of 67.20% on the ExDark dataset, yielding an improvement of 2.39% over the baseline YOLO11n, while reducing computational complexity from 6.3 to 5.8 GFLOPs. TinyDark-YOLO also achieves competitive performance on the DarkFace dataset.</p>

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TinyDark-YOLO for adaptive and lightweight object detection in low-light conditions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月19日

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  • Xuefang Zheng,
  • Lifang Chen,
  • Jiechi Zhang

摘要

Low-light object detection frequently suffers from weak contrast, high noise, and poor performance on small objects. Existing methods often adopt image enhancement to improve detection performance, which results in substantial computational resource consumption. To address these challenges, we propose TinyDark-YOLO, an adaptive and efficient low-light object detection algorithm based on YOLO11. First, we design an Adaptive Gamma Enhancement (AGE) module to adaptively adjust the brightness of input images. Second, we introduce an Attention-based Intra-scale Feature Interaction (AIFI) module to construct global context modeling on high-level features and capture long-range spatial dependencies, improving the detection accuracy of small objects. Finally, we propose a Lite Efficient Head (LEHead) to reduce computational overhead while enhancing the model’s ability to capture weak features in dark environments. We conduct validation and ablation experiments on the ExDark dataset. The results demonstrate that the proposed model achieves an mAP@0.5 of 67.20% on the ExDark dataset, yielding an improvement of 2.39% over the baseline YOLO11n, while reducing computational complexity from 6.3 to 5.8 GFLOPs. TinyDark-YOLO also achieves competitive performance on the DarkFace dataset.