激光技术2024，Vol.48Issue(4)：534-541,8.DOI:10.7510/jgjs.issn.1001-3806.2024.04.011
基于YOLOv5改进的红外目标检测算法
An improved infrared object detection algorithm based on YOLOv5
摘要
Abstract
To address the issues of low recognition accuracy,lack of infrared image features,and poor contrast affecting object detection,several improvements to the original YOLOv5 model were proposed.Firstly,an additional prediction feature layer was introduced to enhance the detection capability for small objects in infrared images.Additionally,a coordinate attention mechanism was employed to enhance the extraction of strong features from infrared targets,thereby improving the detection accuracy of the model.Secondly,the feature fusion network was optimized by using a bidirectional feature pyramid network to improve the model's expressive power and reduce redundant computation.Lastly,to tackle the problem of sample imbalance in detection localization and bounding box regression tasks,the focal-EIOU as the loss function was adopted.This accelerates convergence speed and focuses the regression process on high-quality anchor boxes.Experimental results demonstrate that the improved YOLOv5 achieves an accuracy of 85.3％on the FLIR dataset,which is a 4.2％improvement over the original network model.It not only exhibits high detection accuracy but also provides feasibility for deployment on embedded devices.关键词
图像处理/深度学习/红外目标检测/卷积神经网络/特征融合Key words
image processing/deep learning/infrared object detection/convolutional neural networks/feature fusion分类
电子信息工程引用本文复制引用
刘皓皎,刘力双,张明淳..基于YOLOv5改进的红外目标检测算法[J].激光技术,2024,48(4):534-541,8.基金项目
光电信息控制和安全技术重点实验室基金资助项目(202105509) （202105509）
