LMIENet enhanced object detection method for low light environment in underground mines
The underground working environment of coal mines is complex,with unfavorable factors such as low brightness of artificial light sources,high dust content,and high water vapor density.This leads to difficulties in extracting features and low accuracy of object re-cognition and positioning when existing object detection algorithms are applied to coal mines.An object detection algorithm for low illu-mination environments in coal mines is proposed,which consists of an low-light mine image enhancement module LMIENet and a object detection module.The image enhancement module is used to improve the image quality of the original image,restore various image in-formation,and then use a target detection network to perform specific target detection on the enhanced image,effectively improving the accuracy of detection.In the image enhancement module,a lightweight enhancement parameter prediction network is designed with refer-ence to the zero reference depth curve estimation algorithm,and the pixel level enhancement parameter matrix is calculated for image qual-ity enhancement and brightness adjustment of low light images.The network implicitly measures the image enhancement effect through the designed non-reference loss function,and guides the network to conduct unsupervised learning,Enable the network to adaptively en-hance the image quality of the original image without relying on paired datasets.In the object detection module,the YOLO v8n object de-tection model is adopted,which has a lightweight model size and high flexibility to avoid excessive overall model complexity;Using Fo-cal EIoU Loss to improve regression loss,accelerate model convergence,and improve model detection accuracy.The experimental results show that compared with classic object detection algorithms such as Faster R-CNN,SSD,RetinaNet,etc.,the proposed algorithm per-forms well on the self-made coal mine object detection dataset,and is effective in object detection in low light environments mAP@0.5 reaches 98.0%,mAP@0.5∶0.95 reaches 64.8%,and the running time of a single frame image in the experimental environment is only 11 ms,which is superior to other comparison methods.It is proven that the proposed algorithm can effectively achieve object detection in low illumination and complex environments in coal mines,with short time consumption and high computational efficiency.
low illuminationobject detection of coal mineimage enhancementunsupervised learning
NETL
NSTL
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