工矿自动化2025，Vol.51Issue(4)：107-113,145,8.DOI:10.13272/j.issn.1671-251x.2024110018
基于低光照增强的红外和可见光图像融合的井下人员识别
Underground personnel recognition based on low-light enhancement of infrared and visible light image fusion
摘要
Abstract
In underground environments,there is low light,and personnel features are not clearly visible.Existing infrared and visible light image fusion methods based on deep learning use only infrared information to fill in the scene defects caused by the light degradation of visible images under weak lighting conditions.As a result,rich scene information from visible images is lost in the fused image in dark environments.Moreover,treating image enhancement and image fusion as separate tasks leads to poor fusion results.To address the above issues,a model for underground personnel recognition based on low-light enhancement of infrared and visible light image fusion was proposed.First,the visible and infrared sensor images underwent preprocessing steps such as grayscaling and geometric correction.Then,the processed images were passed into a low-light enhancement network,which removed the illumination component from the degraded visible light images at the feature level.Finally,texture-contrast enhancement networks performed feature-level fusion,enhancing overall visual perception in terms of texture and contrast.Experimental results showed that the proposed model improved underground personnel recognition results compared to the visible light modality,with an average accuracy increase of 8.2％,recall rate increase of 12.5％,and mAP@0.5 increase of 8.3％.Compared to the infrared modality,accuracy increased by an average of 2.1％,recall rate increased by 5.1％,and mAP@0.5 increased by 4.1％.Meanwhile,the detection speed reached 31.2 frames/s,solving problems such as misdetection and missed detection caused by unclear personnel features in low-light underground scenarios.关键词
井下人员识别/低光照增强/红外和可见光图像融合/边缘纹理增强/对比度增强Key words
underground personnel recognition/low-light enhancement/infrared and visible light image fusion/edge texture enhancement/contrast enhancement分类
矿业与冶金引用本文复制引用
南晶晶,潘红光,蒋泽,张立斌,张会鹏..基于低光照增强的红外和可见光图像融合的井下人员识别[J].工矿自动化,2025,51(4):107-113,145,8.基金项目
陕西省秦创原"科学家+工程师"队伍建设项目(2022KXJ-38). （2022KXJ-38）
