Low-Light Object Detection via Joint Image Enhancement and Feature Adaptation

QIAO Chengping; JIN Jiakun; ZHANG Junchao; ZHU Zhengliang; CAO Xiangxu

doi:10.11999/JEIT250302

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QIAO Chengping, JIN Jiakun, ZHANG Junchao, ZHU Zhengliang, CAO Xiangxu. Low-Light Object Detection via Joint Image Enhancement and Feature Adaptation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250302

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QIAO Chengping, JIN Jiakun, ZHANG Junchao, ZHU Zhengliang, CAO Xiangxu. Low-Light Object Detection via Joint Image Enhancement and Feature Adaptation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250302

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Low-Light Object Detection via Joint Image Enhancement and Feature Adaptation

doi: 10.11999/JEIT250302 cstr: 32379.14.JEIT250302

1.
School of Automation, Central South University, Changsha 410083, China
2.
Hunan Provincial Key Laboratory of Optic-Electronic Intelligent Measurement and Control, Changsha 410083, China
3.
College of Marine Information Engineering, Jimei University, Xiamen 361021, China

Funds: The Natural Science Foundation of China (62105372), Fundamental Research Foundation of National Key Laboratory of Automatic Target Recognition (WDZC20255290209), Open Funding of State Key Laboratory of Intelligent Coal Mining and Strata Control (SKLIS202404)

Received Date: 2025-04-25
Rev Recd Date: 2025-08-20

Available Online: 2025-08-28

Abstract

Abstract

Objective Object detection has advanced significantly under normal lighting conditions, supported by numerous high-accuracy, high-speed deep learning algorithms. However, in low-light environments, images exhibit reduced brightness, weak contrast, and severe noise interference, leading to blurred object edges and loss of color information, which substantially degrades detection accuracy. To address this challenge, this study proposes an end-to-end low-light object detection algorithm that balances detection accuracy with real-time performance. Specifically, an end-to-end network is designed to enhance feature quality and improve detection accuracy in real time under low-light conditions. Methods To improve object detection performance under low-light conditions while maintaining detection accuracy and real-time processing, this study proposes an end-to-end low-light image object detection method. Detection accuracy is enhanced through joint learning of image enhancement and feature adaptation, with the overall network structure shown in Fig. 1. First, a data augmentation module synthesizes low-light images from normal-light images. The paired normal-light and low-light images are mixed using the MixUp function provided by YOLOv5 to generate the final low-light images. These synthesized images are input into the low-light image enhancement module. In parallel, the matched normal-light images are provided as supervision to train the image enhancement network. Subsequently, both the enhanced low-light images and the corresponding normal-light images are fed into the object detection module. After processing through the YOLOv5 backbone, a matching loss is computed to guide feature adaptation. Result and Discussions The experiments are conducted primarily on the Polar3000 and LLVIP datasets. Fig. 3 presents the detection results obtained using YOLOv5 with different image enhancement methods applied to the Polar3000 dataset. Most existing methods tend to misclassify overexposed regions as bright Unmanned Aerial Vehicles (UAVs). In contrast, the proposed method demonstrates accurate object detection in low-light conditions without misidentifying overexposed areas as UAVs (Fig. 3). Furthermore, the detection performance of the proposed method, termed MAET, is compared with that of a standalone YOLOv5 model. Quantitative experiments show that the proposed method outperforms both image-enhancement-first detection pipelines and recent low-light object detection methods across both experimental groups A and B, irrespective of low-light fine-tuning. On the LLVIP dataset, the proposed method achieves a detection accuracy of 91.7% (Table 1), while on the Polar3000 dataset, it achieves 92.3% (Table 2). The model also demonstrates superior generalization performance on the ExDark and DarkFace datasets (Tables 4 and 3). Additionally, compared to the baseline YOLOv5 model, the proposed method increases parameter size by only 2.5% while maintaining real-time detection speed (Table 5). Conclusions This study proposes a low-light object detection method based on joint learning of image enhancement and feature adaptation. The approach simultaneously optimizes image enhancement loss, feature matching loss, and object detection loss within an end-to-end framework. It improves image illumination, preserves fine details, and aligns the features of enhanced images with those acquired under normal lighting conditions, enabling high-precision object detection in low-light environments. Comparative experiments on the LLVIP and Polar3000 datasets demonstrate that the proposed method achieves improved detection accuracy while maintaining real-time performance. Furthermore, the method achieves the best generalization results on the ExDark and DarkFace datasets. Future work will explore low-light object detection based on multimodal data fusion of visible and infrared images to further enhance detection performance in extremely dark conditions.
- Low-Light Images,
- Object Detection,
- Feature Matching,
- Image enhancement Network

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References(24)

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