红外技术2025，Vol.47Issue(6)：739-747,9.
用于野外机器人的红外小目标检测算法
Infrared Small Target Detection Algorithm for Field Robots
摘要
Abstract
Infrared(IR)thermal imaging target detection is essential for enabling robots to conduct all-weather inspections in field environments.This paper addresses two key challenges:the limited computing power of embedded systems onboard robots for real-time detection,and the low resolution of small targets in thermal imaging.To address these challenges,a lightweight detection algorithm based on an improved YOLOv7 framework is proposed.First,the network structure is pruned to enhance real-time performance on embedded devices.Subsequently,the backbone is optimized by integrating adaptive convolutional layers and a batchless normalization module.To improve small-target detection accuracy,multi-rate dilated 3D convolution is used to extract high-resolution scale-sequence features,which are subsequently fused via a Feature Pyramid Network(FPN).Finally,the SIoU-based position regression method is introduced in the prediction stage to improve regression speed and accuracy.Experimental validation on the NVIDIA Jetson Xavier NX platform using a nighttime thermal imaging dataset shows a 162％ improvement in FPS,with only a 1.95％ reduction in mAP compared to the original YOLOv7,meeting the requirements for real-time detection.关键词
YOLOv7算法/小目标检测/红外热成像/多速率空洞卷积/3D卷积Key words
YOLOv7 algorithm/small-target detection/infrared thermal imaging/multi-rate dilated convolution/3D convolution分类
计算机与自动化引用本文复制引用
童金鑫,蒋刚,黄凯睿,陈清平,徐文刚..用于野外机器人的红外小目标检测算法[J].红外技术,2025,47(6):739-747,9.基金项目
四川省科技计划重点研发基金项目(2021YFG0075,2021YFG0076,2022YFG0347) （2021YFG0075,2021YFG0076,2022YFG0347）
四川省科技计划重点基金项目(2021JDKP0075). （2021JDKP0075）
