基于改进YOLOv7的红外安防目标检测
Infrared security object detection based on improved YOLOv7
韩瑶 1骆晓玲 1程换新 1沈静2
作者信息
- 1. 青岛科技大学,青岛 266061
- 2. 湖北大学,武汉 430062
- 折叠
摘要
针对安防场景中红外图像对比度低、目标轮廓不明显导致目标检测效果差的问题,提出一种基于改进YOLOv7的红外安防目标检测算法.采用递归门控卷积改进主干网络,增强对输入图像高阶信息交互能力;使用SimAM注意力机制构建ELAN-S模块,降低信息丢失率的同时减少网络参数;使用K-means++聚类算法优化锚盒尺寸,提高检测精度.对InfiRay公开数据集进行数据增强和模型验证实验,结果表明,提出的算法在保持较高检测速度前提下,平均精度均值达到了 87.15％,相对于原YOLOv7网络与其他主流算法有明显提高,证明改进方法先进有效.
Abstract
To address the problem of poor object detection due to low contrast and inconspicuous object contours in infrared images in security scenes,an improved YOLOv7-based infrared security object detection algorithm is pro-posed.The recursive gated convolution is used to improve the backbone network and enhance the ability to interact with higher-order information of the input image;the ELAN-S module is constructed using the Sim AM attention mech-anism to reduce the information loss rate while reducing the network parameters;the anchor box size is optimized using the K-means++clustering algorithm to improve the detection accuracy.Data enhancement and experiments are con-ducted on the InfiRay public dataset,and the results show that the algorithm proposed in this paper has an mAP value of 87.15％while maintaining a high detection speed,which is a significant improvement compared with the original YOLOv7 network and other mainstream algorithms,proving that the improved method is advanced and effective.
关键词
目标检测/红外图像/YOLOv7/递归门控卷积/SimAMKey words
object detection/infrared images/YOLOv7/Recursive Gate Convolution/SimAM引用本文复制引用
基金项目
国家海洋局重大专项(国海科字[2016]494号:30)
出版年
2024
NETL
NSTL
万方数据