计算机工程与应用2024，Vol.60Issue(1)：74-83,10.DOI:10.3778/j.issn.1002-8331.2305-0312
改进YOLOv7-tiny的轻量级红外车辆目标检测算法
Improved YOLOv7-tiny Lightweight Infrared Vehicle Target Detection Algorithm
摘要
Abstract
In order to solve the problems of large number of parameters and computation,low recognition accuracy,and difficult small target detection in infrared scene,a lightweight infrared vehicle target detection algorithm with improved YOLOv7-tiny is proposed:KD-YOLO-DW.Firstly,the ELAN-DW module is proposed by merging deep separable convolution,which greatly reduces the number of network parameters and the amount of computation.Secondly,by intro-ducing GhostNet V2 module in the feature fusion layer,the fusion ability of different scale features is improved.The WIoU loss function of dynamic non-monotone FM is used to solve the problem of imbalanced samples in the infrared data set,and the detection ability of the lightweight algorithm is improved.Then,a cross-scale fusion strategy is proposed in combination with residual idea,which improves the detection effect of lightweight algorithm on different scale targets and reduces the missing rate of small targets.Finally,the lightweight model is reconcentrated by knowledge distillation,which further improves the accuracy of the model for detecting infrared targets.The experimental results show that compared with the YOLOv7-tiny model,KD-YOLO-DW model has 24.6%and 16.7%fewer parameters and 16.7%less computation,the model size is only 9.2 MB,and mAP is increased by 3.27 and 3.15 percentage points,respectively,with smaller model volume and better detection effect.关键词
红外目标检测/轻量级/知识蒸馏/损失函数/YOLOv7-tiny/GhostNet V2Key words
infrared target detection/lightweight/knowledge distillation/loss function/YOLOv7-tiny/GhostNet V2分类
信息技术与安全科学引用本文复制引用
许晓阳,高重阳..改进YOLOv7-tiny的轻量级红外车辆目标检测算法[J].计算机工程与应用,2024,60(1):74-83,10.基金项目
国家自然科学基金(12071367) （12071367）
陕西省"特支计划"青年拔尖人才项目(289890259). （289890259）
