工矿自动化2026，Vol.52Issue(4)：68-77,10.DOI:10.13272/j.issn.1671-251x.2026030044
面向煤矿井下复杂光照环境的安全装备小目标检测模型
Small object detection model for safety equipment under complex lighting conditions in underground coal mines
摘要
Abstract
Existing small object detection methods for safety helmets,self-rescuers,and mining lamps show insufficient adaptability to small targets and unstable sample matching during training under underground coal mine conditions where low light and backlight coexist.Based on the YOLOv11n framework,a small object detection model for complex lighting conditions in underground coal mines,named LSD-YOLO,was proposed.In the neck network,a Lighting-Aware Spatial and Channel Adaptive Modulation(LASCAM)module was introduced to perform channel-wise affine compensation and spatial saliency modulation for feature responses under low-light and backlight scenarios.A Frequency-Aware Small-Object Pyramid Module(FSPM)was designed to enhance the detail representation of small objects through multi-scale frequency decomposition and high-frequency modulation.A Low-Light and Small-Object Detection Friendly Loss(LSD-Loss)was designed to enhance the learning signal of valid samples,and a Scale-Adaptive Task-Aligned Distribution(SATAD)strategy was introduced so that the positive sample matching process was adaptively adjusted according to object scale,thereby improving training stability and the utilization efficiency of small object samples.The results showed that LSD-YOLO achieved excellent detection performance,with mAP@0.5 reaching 91.6％,outperforming all comparison models.Compared with the baseline model YOLOv11n,the precision,recall,and mAP@0.5 of LSD-YOLO increased by 0.9％,1.2％,and 3.7％,respectively,effectively improving detection performance in complex underground scenarios.In terms of model complexity,LSD-YOLO had 4.1×106 parameters and 8.7 GFLOPs,which were much lower than those of RT-DETR-R18 and YOLOv11s,and the inference speed reached 104.1 frames/s,which met real-time detection requirements.The mAP@0.5 of LSD-YOLO was improved by 0.1％and 0.2％compared with RT-DETR-R18 and YOLOv11s,respectively,indicating a good balance between detection accuracy and model complexity.关键词
安全装备/小目标检测/YOLOv11n/光照感知空间-通道自适应调制/频率感知小目标金字塔/弱光小目标友好损失函数/尺度自适应任务对齐分配策略Key words
safety equipment/small object detection/YOLOv11n/Lighting-aware spatial and channel adaptive modulation/Frequency-aware small-object pyramid/Low-light and small-object detection friendly loss/Scale-adaptive task-aligned distribution strategy分类
矿业与冶金引用本文复制引用
沈凌志,李权,王鹏,季亮,吴航海,王琪..面向煤矿井下复杂光照环境的安全装备小目标检测模型[J].工矿自动化,2026,52(4):68-77,10.基金项目
国家重点研发计划项目(2023YFC3009102-4) （2023YFC3009102-4）
天地科技股份有限公司全重实验室项目(2025-TD-QZ004) （2025-TD-QZ004）
天地(常州)自动化股份有限公司科研项目(2025TY2002). （常州）
