计算机工程与应用2024，Vol.60Issue(10)：237-245,9.DOI:10.3778/j.issn.1002-8331.2308-0196
改进YOLOv7的城市小型无人机目标检测方法
Improved YOLOv7 Target Detection Method for Small Urban UAVs
摘要
Abstract
Countermeasures against"low and small moving"UAVs have become an important tool for low altitude airspace security defense,but real-time detection and accurate identification are the prerequisite and key foundation for effective countermeasures.Aiming at the urban low-altitude environment,the target detection algorithm has low accuracy in detecting small-scale UAV targets in different backgrounds,is prone to omission and misdetection,and is susceptible to interference from external factors,etc.,a"low and small moving"UAV target detection method based on the improved YOLOv7 is proposed.Firstly,a large number of UAV samples from different environments and backgrounds are collected to build a data set and are pre-processed by ViBe(visual background extractor)algorithm.Secondly,the coordinate atten-tion mechanism and SPDConv(space-to-depth convolution)module are introduced to improve and optimize the network structure of YOLOv7.Finally,a secondary detection architecture is proposed to fuse ViBe and improved YOLOv7,and the improved YOLOv7 is used as the network model to detect the images processed by ViBe.Based on the position size relationship between the original image and the processed image,the detected target coordinates are mapped back to the original image,so as to complete the target detection and extraction.The experimental results show that the detection accuracy of the proposed target detection method reaches 96.5%,which is 15.8 percentage points higher than that of the original YOLOv7 method,significantly improving the detection accuracy of"low and small moving"targets and meeting the demand for real-time accurate detection of low-altitude UAVs.关键词
ViBe算法/反无人机/YOLOv7/坐标注意力机制/小目标检测/SPDConvKey words
ViBe algorithm/anti-drone/YOLOv7/coordinate attention mechanism/small target detection/SPDConv分类
信息技术与安全科学引用本文复制引用
崔勇强,李嘉轩,侯林果,梅涛,白迪,陈少平..改进YOLOv7的城市小型无人机目标检测方法[J].计算机工程与应用,2024,60(10):237-245,9.基金项目
国家自然科学基金(62201621) （62201621）
湖北省自然科学基金(2022CFC050). （2022CFC050）
