SPB-YOLO:一种高效的无人机图像实时检测器

Xinran Wang, Weihong Li, Wei Guo, Kun Cao
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引用次数: 15

摘要

近年来,利用无人机(UAV)进行图像采集已成为一种流行的应用。然而,无人机图像的大尺度变化和密集目标分布特性给目标检测带来了挑战。因此,我们提出了一种高效的无人机图像端到端探测器SPB-YOLO。本文首先设计了条带瓶颈(SPB)模块,利用注意机制提高无人机图像中不同尺度目标的检测灵敏度,从而更好地理解宽度高度依赖性;其次,提出了一种基于路径聚合网络(PANet)的特征图上采样策略,并在YOLOv5的基础上增加了一个检测头,专门处理密集目标分布的检测任务;最后,我们在两个公开数据集上进行了实验,结果表明SPBYOLO算法优于其他最新的无人机图像检测器,并且在检测精度和速度之间取得了很好的平衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SPB-YOLO: An Efficient Real-Time Detector For Unmanned Aerial Vehicle Images
Recently, using unmanned Aerial Vehicle(UAV) to capture images has become a popular application. However, the large scale variation and dense object distribution characteristic of UAV images brings challenges to object detection. Hence, we propose an efficient end-to-end detector named SPB-YOLO for UAV images. In this paper, firstly we design a Strip Bottleneck (SPB) module to better understand the width-height dependency by using an attention mechanism for improving the detection sensitivity of different scales’ objects in the UAV image. Secondly, we propose an upsample strategy based on Path Aggregation Network(PANet) for the feature map and add another one detection head compared to YOLOv5, which specially deal with the detection task of dense objects distribution. Finally, we execute some experiments on two public datasets, and the results show that the proposed SPBYOLO outperforms other latest UAV image detectors and makes a good trade-off between detection accuracy and speed.
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