计算机工程与应用2026，Vol.62Issue(7)：121-130,10.DOI:10.3778/j.issn.1002-8331.2504-0183
改进YOLOv11s的距离选通图像人脸检测算法
Improved YOLOv11s for Gated Imaging Face Detection Algorithm
摘要
Abstract
Laser range-gated imaging technology enables long-range object detection,partial penetration imaging,and reliable operation in adverse weather conditions such as rain,snow,and fog.To address challenges including local feature loss and noise interference in complex environments like through windows,occlusions,smoke,or flames,this study devel-ops an enhanced face detection algorithm based on YOLOv11s,optimized for range-gated imaging characteristics.First,the CSDSM module replaces C2PSA to improve fine-grained feature preservation while maintaining training efficiency.Then,the MultiSEAM(multi-scale separation and enhancement attention module)enhances the neck network's ability to handle occlusions and understand contextual features.Furthermore,the SPD-Conv module strengthens low-resolution fea-ture extraction in the backbone network.Finally,an improved EMF module expands local perception and semantic repre-sentation for small objects.Experimental results demonstrate performance gains of 3.3 percentage points in mAP@0.5 and 1.7 percentage points in mAP@0.5:0.95 on range-gated image datasets,confirming the method's effectiveness.To fur-ther verify the generalization and universality of the improved algorithm,the VOC2007 dataset is also selected for testing.The results show that the mAP@0.5 and mAP@0.5:0.95 metrics are improved by 1.8 percentage points and 2.2 percent-age points,respectively.关键词
选通图像/人脸检测/YOLOv11s/复杂环境Key words
gated imaging/face detection/YOLOv11s/complex environment分类
信息技术与安全科学引用本文复制引用
张正,赵海明,田青..改进YOLOv11s的距离选通图像人脸检测算法[J].计算机工程与应用,2026,62(7):121-130,10.基金项目
国家重点研发计划(2024QY2632). （2024QY2632）
