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TinyDark-YOLO for adaptive and lightweight object detection in low-light conditions.
Xuefang Zheng1, Lifang Chen2, Jiechi Zhang3
1School of Microelectronics, Jiangsu Vocational College of Information Technology, Wuxi, 214153, China.
Scientific Reports
|June 19, 2026
Summary
TinyDark-YOLO enhances low-light object detection by adaptively adjusting brightness and improving small object accuracy. This efficient algorithm reduces computational load while boosting performance on challenging dark datasets.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Low-light object detection faces challenges like poor contrast, noise, and difficulty detecting small objects.
- Current image enhancement methods for detection are computationally expensive.
Purpose of the Study:
- To develop an adaptive and efficient algorithm for low-light object detection.
- To improve the accuracy of detecting small objects in dark environments.
- To reduce the computational overhead of low-light object detection models.
Main Methods:
- Proposed TinyDark-YOLO algorithm based on YOLOv11.
- Introduced an Adaptive Gamma Enhancement (AGE) module for brightness adjustment.
- Developed an Attention-based Intra-scale Feature Interaction (AIFI) module for global context and small object detection.
- Implemented a Lite Efficient Head (LEHead) to minimize computational cost and enhance weak feature capture.
Main Results:
- Achieved an mAP@0.5 of 67.20% on the ExDark dataset, a 2.39% improvement over the baseline YOLOv11n.
- Reduced computational complexity from 6.3 to 5.8 GFLOPs.
- Demonstrated competitive performance on the DarkFace dataset.
Conclusions:
- TinyDark-YOLO offers an effective and efficient solution for low-light object detection.
- The proposed modules (AGE, AIFI, LEHead) contribute to improved accuracy and reduced computational complexity.
- The algorithm shows promise for real-world applications in challenging lighting conditions.
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