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YOLO-SCC: A Lightweight Object Detection Method for Mine Safety
Zhe Wu1, Xuanrui Zhang1, Yanping Cui1
1School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.
Sensors (Basel, Switzerland)
|August 13, 2026
Summary
This study introduces YOLO-SCC, a lightweight object detection model designed for personnel detection in challenging underground coal mine environments. YOLO-SCC enhances feature representation and target recognition, improving accuracy in complex conditions.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Mining Engineering
Background:
- Personnel detection in underground coal mines faces challenges like poor lighting, dust, and complex backgrounds.
- Existing methods struggle with scale variation and occlusions, impacting safety and efficiency.
Purpose of the Study:
- To develop a lightweight object detection model (YOLO-SCC) for accurate personnel detection in underground coal mines.
- To enhance feature representation and target recognition capabilities in complex mining environments.
Main Methods:
- Proposed YOLO-SCC based on YOLO26n, incorporating SPDConv, CBAM, and ContextAggregation modules.
- SPDConv optimizes downsampling for feature detail preservation.
- CBAM adaptively weights features for improved target focus.
- ContextAggregation enhances semantic understanding for complex scenes.
Main Results:
- YOLO-SCC achieved 88.8% precision, 82.2% recall, 85.3% mAP50, and 51.2% mAP50-95.
- Demonstrated significant improvements over the baseline YOLO26n with minimal increase in model size and computational cost.
- Showcased enhanced false-positive suppression and localization accuracy.
Conclusions:
- YOLO-SCC effectively addresses personnel detection challenges in underground coal mines.
- The model maintains a lightweight scale while improving detection performance.
- The proposed enhancements are crucial for safety and operational monitoring in mining environments.
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