基于YOLO算法的红外图像目标检测的改进方法

传统红外图像行人检测方法利用人工进行比例模板设计和行人轮廓特征提取,由于预设模板比例相对固定,当行人因衣着增减、随身携带物品及姿态改变等原因使其轮廓比例发生较大变化时,往往会导致算法失灵而出现漏检现象。而基于深度学习的目标检测则通过对大量样本的本质特征进行抽象、提取、加工和整合,进而实现对更多样特征的学习。因此利用深度学习目标检测算法进行红外图像行人检测应用的研究可以弥补传统检测方法的不足。YOLOv3是目前性能较为均衡的识别算法,本文在分析YOLOv3系列算法的原理和特点的基础上提出了一个新的改进算法模型——Darknet-19-yolo-3,在几乎不损失检测精度的条件下提升检测速度,一定程度上实现检测准确率和速度的相对平衡。

An improved method of infrared image target detection based on YOLO algorithm

The traditional infrared image pedestrian detection method uses artificial scale template to design and extract pedestrian contour feature.Because the preset template proportion is relatively fixed,when the pedestrian''s contour proportion changes greatly due to the increase or decrease of clothing,the change of carrying on items and postures,the direction will lead to the algorithm failure and the phenomenon of missing detection.The target detection based on deep learning can realize the learning of more diverse features by abstracting,extracting,processing and integrating the essential features of a large number of samples.Therefore,the research on the application of deep learning target detection algorithm in infrared image pedestrian detection can make up for the shortcomings of traditional detection methods.YOLOv3 is a relatively balanced recognition algorithm at present.Based on the analysis of the principle and characteristics of YOLOv3 series algorithm,this paper proposes a new improved algorithm model,which can improve the detection speed with almost no loss of detection accuracy.Thus,to a certain extent,the relative balance of detection accuracy and speed is achieved.