无人机航拍图像轻量化多目标检测算法

IF 1.4 4区 地球科学 Q4 ENVIRONMENTAL SCIENCES
Yang Liu, Ding Ma, Yongfu Wang
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引用次数: 0

摘要

与在自然场景中捕获的图像相比,无人机航拍获得的图像具有更复杂的背景和许多密集的小目标,这对目标检测算法的检测精度提出了更高的要求。然而,由于无人机是一种小型移动设备,如何保证其实时检测效果一直是一个问题。针对这些问题,设计了轻量级的YOLOv7算法,即LRT-YOLOv7。首先,提出增强特征融合模块和变压器高效层聚合网络模块,改进特征提取和融合性能,提高小目标检测效率;其次,针对无人机图像中目标尺寸小、背景复杂的问题,在YOLOv7-tiny算法中重新设计了检测头结构,增强了算法的多尺度特征融合能力,从而提高了算法对小目标的检测精度。最后,以精密度、查全率、平均精密度和帧数每秒(FPS)为评价指标,进行消融、对比和可视化验证实验。结果表明,LRT-YOLOv7算法在自制流量目标数据集上的检测速度为133.8 FPS,精度指标为84.58%。因此,LRT-YOLOv7算法在无人机航拍交通目标检测任务中具有较高的精度和实时性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lightweight multi-target detection algorithm for unmanned aerial vehicle aerial imagery
Compared with the image captured in the natural scene, the image obtained by unmanned aerial vehicle (UAV) aerial photography has a more complex background and many dense small targets, which puts forward higher requirements for the detection accuracy of the target detection algorithm. However, because the UAV is a kind of small mobile device, how to ensure its real-time detection effect has been a problem. Aiming at these problems, the lightweight YOLOv7 algorithm, namely LRT-YOLOv7, is designed. First, the enhance feature fusion module and the transformer efficient layer aggregation networks module are proposed to improve the performance of feature extraction and fusion to enhance the efficiency of small target detection. Second, aiming at the problems of small target size and complex background in the UAV images, the detection head structure is redesigned in the YOLOv7-tiny algorithm to enhance the multi-scale feature fusion ability of the algorithm and thereby improve the algorithm’s detection accuracy for small targets. Finally, ablation, comparison, and visualization validation experiments were conducted using precision, recall, mean average precision, and frames per second (FPS) as evaluation indicators. The results show that the detection speed of the LRT-YOLOv7 algorithm on the self-made traffic target dataset is 133.8 FPS, and the precision indicator is 84.58%. Therefore, the LRT-YOLOv7 algorithm has high accuracy and real-time performance in traffic target detection tasks for UAV aerial imagery.
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来源期刊
Journal of Applied Remote Sensing
Journal of Applied Remote Sensing 环境科学-成像科学与照相技术
CiteScore
3.40
自引率
11.80%
发文量
194
审稿时长
3 months
期刊介绍: The Journal of Applied Remote Sensing is a peer-reviewed journal that optimizes the communication of concepts, information, and progress among the remote sensing community.
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