[1]文 青,伍 欣,敖 斌,等.基于航空图像的目标检测算法 Trans_YOLOv5[J].计算机技术与发展,2024,34(01):77-82.[doi:10. 3969 / j. issn. 1673-629X. 2024. 01. 012]
　WEN Qing,WU Xin,AO Bin,et al.Target Detection Algorithm Trans_YOLOv5 Based on Aerial Image[J].,2024,34(01):77-82.[doi:10. 3969 / j. issn. 1673-629X. 2024. 01. 012]
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基于航空图像的目标检测算法 Trans_YOLOv5()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年01期
页码:
77-82
栏目:
媒体计算
出版日期:
2024-01-10

文章信息/Info

Title:
Target Detection Algorithm Trans_YOLOv5 Based on Aerial Image
文章编号:
1673-629X(2024)01-0077-06
作者:
文 青; 伍 欣; 敖 斌; 李 宽; 殷建平
东莞理工学院 网络空间安全学院,广东 东莞 523808
Author(s):
WEN Qing; WU Xin; AO Bin; LI Kuan; YIN Jian-ping
School of Cyberspace Security,Dongguan University of Technology,Dongguan 523808,China
关键词:
小目标检测; 航空图像; YOLOv5; 圆形平滑标签; Swin Transformer
Keywords:
small target detection; aerial images; YOLOv5; circular smooth label; Swin Transformer
分类号:
TP391. 41
DOI:
10. 3969 / j. issn. 1673-629X. 2024. 01. 012
摘要:
与自然图像的检测算法相比较,航空图像的检测存在目标角度随机、目标尺度变化剧烈、小目标密集、图像背景复杂等问题。 针对这一系列难题,提出适用于航空图像检测的
Trans-YOLOv5 算法。 修改 YOLOv5 算法中数据预处理模块以及后处理方法,增加一个目标角度标签的处理,使其适用于目标角度随机的航空图像。 针对后续出现的边界问题,
引入CSL(Circular Smooth Label,圆形平滑标签) 将标签角度回归问题转换为分类问题,提高角度标签检测的精度。 针对航空图像小目标检测问题,将 Swin Transformer 集成于
YOLOv5 框架中,提升模型对小目标的检测效果,并配合注意力机制模块,提高全局表征能力,使网络模型更加关注于待检测的目标对象。 在 DOTAv2. 0 航空图像数据集上的实验
结果验证了所提方法的有效性,检测结果达到 60. 98% mAP,与原 YOLOv5 算法检测结果相比提高 10. 85 百分点,与官网公布的竞赛最佳结果相比提高 2. 01 百分点。
Abstract:
Compared with the detection algorithm of natural images,there are problems such as random target angle,sharp change of targetscale,dense small targets,and complex image background in aerial image target detection. Trans-YOLOv5 algorithm suitable for aerialimage detection is proposed to solve this series of problems. Modifying the data preprocessing module and post-processing method in theYOLOv5 algorithm to add the processing of a target angle label to make it suitable for aerial images with random target angles. CSL( Circular Smooth Label) is introduced to transform the label angle regression issue into a classification issue about the problem ofboundary problems. Regarding the issue of small target detection in aerial images, we integrate Swin Transformer into the YOLOv5framework to capture global semantic information,which improve the detection effect of the model on small targets,and cooperate withthe attention mechanism module to improve the global representation ability,so that the network model pays more attention to the targetobject to be detected. The experimental results on the DOTAv2. 0 dataset validate the effectiveness of the proposed method. Thedetection results reach 60. 98% mAP,which
is 10. 85 percentage points higher than that of the original YOLOv5 algorithm and 2. 01 percentage points higher than the competition results published on the official website.

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更新日期/Last Update: 2024-01-10