计算机工程与应用2024，Vol.60Issue(8)：173-181,9.DOI:10.3778/j.issn.1002-8331.2305-0264
改进YOLOv7的航拍图像目标检测
Improved YOLOv7 for UAV Image Object Detection
摘要
Abstract
Aerial image target detection has significant practical implications for efficient interpretation of aerial images and applications in mapping,resource inventory,urban and rural planning,etc.To address challenges in UAV aerial images,such as varying object scales,background interference,and missing detection of small targets,propose an improved algo-rithm called AirYOLOv7,based on YOLOv7.Firstly,AirYOLOv7 combines a three-dimensional attention mechanism during feature extraction and a channel attention mechanism during feature fusion in the original network.These mecha-nisms help the model focus on crucial information in the image.Secondly,because of the prevalence of small objects in aerial images,the algorithm adds an additional prediction head for detecting small objects.The algorithm also incorpo-rates the C3STB before each prediction head to improve detection capability for objects of different scales.Additionally,the algorithm addresses the sensitivity of the IoU loss to positional deviations for small objects by introducing the Wasser-stein distance into the original bounding box regression loss.This measure helps improve the detection capability for small objects.Experimental results demonstrate that the effectiveness of AirYOLOv7 on two publicly available optical aerial datasets,DOTA and VisDrone achieves mean average precision of 78.65%and 51.79%on these datasets,respectively,showing improvements of 1.92 percentage points and 2.28 percentage points comparing to the original YOLOv7 which validates the effectiveness of the proposed improvements on optical aerial images.关键词
目标检测/航拍图像/注意力机制/损失函数/Swin Transformer/YOLOv7Key words
object detection/UAV images/attention mechanism/loss function/Swin Transformer/YOLOv7分类
信息技术与安全科学引用本文复制引用
邹振涛,李泽平..改进YOLOv7的航拍图像目标检测[J].计算机工程与应用,2024,60(8):173-181,9.基金项目
国家自然科学基金(61462014). （61462014）
