红外技术2025，Vol.47Issue(5)：591-600,10.
基于改进YOLOv8复杂街道场景下的红外目标检测算法
Infrared Target Detection Algorithm Based on Improved YOLOv8 in Complex Street Scenes
摘要
Abstract
Aiming at the problem of target misdetection and missed detection in infrared images under complex street backgrounds due to factors such as occlusion and lack of texture details,this paper proposes an infrared target detection algorithm for complex street scenes.Using YOLOv8n as the baseline model,firstly,a multi branch convolutional structure is designed to enhance feature extraction and expression.Structural reparameterization is used to decouple the training and inference stages,improve the inference speed of the model,and global self attention estimation is introduced to accelerate the calculation of attention.The time complexity is reduced to O(n),enabling the convolutional kernel attention to achieve dynamic identity.Secondly,combining the advantages of depthwise separable convolution and deformable convolution,after feature fusion between the upsampling results and the output features of the backbone network,a salient information aware deformable convolution attention gating mechanism is introduced to improve the semantic information richness of the fused features.Finally,An efficient intersection and union ratio replace the localization loss function,calculate the length and width influence factors of the predicted box and the true box separately,and accelerate the convergence speed.Validation experiments were conducted on the Flir dataset,and the average accuracy of the improved algorithm reached 79.5％,which is 3.9％higher than the YOLOv8n algorithm.This validates the superiority of the proposed algorithm in infrared target detection under complex street backgrounds.关键词
红外目标/街道场景/WIoU/全局自注意力估计/可变形卷积Key words
infrared targets/street scenes/WIoU/global self-attention estimation/deformable convolution分类
计算机与自动化引用本文复制引用
洪俐,曾祥进..基于改进YOLOv8复杂街道场景下的红外目标检测算法[J].红外技术,2025,47(5):591-600,10.基金项目
国家自然科学基金(61502354) （61502354）
湖北省湖北三峡实验室创新基金(SC215001). （SC215001）
