基于YOLOv5s的遥感图像的车辆小目标检测

摘要/Abstract

摘要： 由于YOLOv5s检测效果好、计算复杂度低而被广泛应用于各类目标检测任务，但是其过大的下采样步长导致对卫星遥感图像中的车辆小目标检测难以获得满意的结果。为了提升对小目标检测的性能，基于YOLOv5s采取降低下采样步长的策略以保护车辆小目标的纹理和几何特性，同时在检测头前插入注意力机制模块以抑制复杂背景对目标的干扰。在0.5 m/pixel分辨率的自建数据集上进行测试，提出的SA-YOLOv5s对车辆目标检测的AP、Recall、Precision值分别达到90.1%、89%和 87.3%，与YOLOv5s相比分别提升了16.4、6和5个百分点，表现出良好的检测性能。

关键词: 遥感图像, 小目标检测, YOLOv5, 自主数据集

Abstract: Because of its good detection effect and low computational complexity， YOLOv5s is widely used in various target detection tasks. However， its large downsampling stride makes it difficult to obtain satisfactory results for small-sized vehicle detection in satellite remote sensing images. In order to improve the performance of small target detection， on the basis of YOLOv5s， the strategy of reducing the downsampling stride is adopted to protect the texture and geometric features of small targets in the vehicle， and the attention mechanism module is inserted in front of the detection head to suppress the interference of complex background. The tested results on the autonomous data set with a resolution of 0.5 m/pixel show that the AP， recall and precision of SA-YOLOv5s for vehicle target detection reached 94.1%， 99% and 87.3% respectively， which were 16.4， 6 and 5 percentage points higher than YOLOv5s， and showed good detection performance.

Key words: remote sensing image, small object detection, YOLOv5, autonomous dataset

邱地发, 于淑芳, 刘锦辉, 毕梦昭. 基于YOLOv5s的遥感图像的车辆小目标检测[J]. 计算机与现代化, 2023, 0(05): 122-126.

QIU Di-fa, YU Shu-fang, LIU Jin-hui, BI Meng-zhao. Improved YOLOv5s for Small Vehicle Object Detection on Remote Sensing Image[J]. Computer and Modernization, 2023, 0(05): 122-126.

参考文献［28］

［1］	LIN T Y， MAIRE M， BELONGIE S， et al. Microsoft COCO: Common objects in context［C］// European Conference on Computer Vision. Springer， 2014:740-755.
［2］	EVERINGHAM M， GOOL L， WILLIAMS C K， et al. The pascal visual object classes （VOC） challenge［J］. International Journal of Computer Vision， 2010，88（2）:303-338.
［3］	RUSSAKOVSKY O， DENG J， SU H， et al. ImageNet large scale visual recognition challenge［J］. International Journal of Computer Vision， 2015，115（3）:211-252.
［4］	CHENG M H， LI X Q， ZHU F X， et al. Application of remote sensing big data technology in refined urban management［C］// 2020 International Conference on Big Data， Artificial Intelligence and Internet of Things Engineering （ICBAIE）. 2020:397-400.
［5］	SIMONYAN K， ZISSERMAN A. Very deep convolutional networks for large-scale image recognition［J］. arXiv preprint arXiv:1409.1556， 2014.
［6］	KRIZHEVSKY A， SUTSKEVER I， HINTON G E. ImageNet classification with deep convolutional neural networks［J］. Communications of the ACM， 2017，60（6）:84-90.
［7］	SZEGEDY C， LIU W， JIA Y Q， et al. Going deeper with convolutions［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015:1-9.
［8］	GIRSHICK R， DONAHUE J， DARRELL T， et al. Rich feature hierarchies for accurate object detection and semantic segmentation［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014:580-587.
［9］	GIRSHICK R. Fast R-CNN［C］// Proceedings of the IEEE International Conference on Computer Vision. 2015:1440-1448.
［10］	REN S Q， HE K M， GIRSHICK R， et al. Faster R-CNN: Towards real-time object detection with region proposal networks［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence， 2017，39（6）:1137-1149.
［11］	REDMON J， DIVVALA S， GIRSHICK R， et al. You only look once: Unified， real-time object detection［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016:779-788.
［12］	REDMON J， FARHADI A. YOLO9000: Better， faster， stronger［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:7263-7271.
［13］	REDMON J， FARHADI A. YOLOV3: An incremental improvement［J］. arXiv preprint arXiv:1804.02767， 2018.
［14］	BOCHKOVSKIY A， WANG C， LIAO H M. YOLOv4: Optimal speed and accuracy of object detection［J］. arXiv preprint arXiv:2004.10934， 2020.
［15］	LI C Y， LI L L， JIANG H L， et al. YOLOv6: A single-stage object detection framework for industrial applications［J］. arXiv preprint arXiv:2209.02976， 2022.
［16］	WANG C Y， BOCHKOVSKIY A， LIAO H M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors［J］. arXiv preprint arXiv:2207.
	02696， 2022.
［17］	LIU W， ANGUELOV D， ERHAN D， et al. SSD: Single shot multibox detector［C］// European Conference on Computer Vision. Springer， 2016:21-37.
［18］	FU C Y， LIU W， RANGA A， et al. DSSD: Deconvolutional single shot detector［J］. arXiv preprint arXiv:1701.06659， 2017.
［19］	LIN T Y， DOLLAR P， GIRSHICK R， et al. Feature pyramid networks for object detection［C］ // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:2117-2125.
［20］	RABBI J， RAY N， SCHUBERT M， et al. Small-object detection in remote sensing images with end-to-end edge-enhanced GAN and object detector network［J］. Remote Sensing， 2020，12（9）:1432-1459.
［21］	GOODFELLOW I J， POUGET-ABADIE J， MIRZA M， et al. Generative adversarial networks［J］. arXiv preprint arXiv:1406.2661， 2014.
［22］	LIM J S， ASTRID M， YOON H J， et al. Small object detection using context and attention［C］// International Conference on Artificial Intelligence in Information and Communication （ICAIIC）. 2021:181-186.
［23］	高新波，莫梦竟成，汪海涛，等. 小目标检测研究进展［J］. 数据采集与处理， 2021，36（3）:391-417.
［24］	YUN S， HAN D Y， OH S J， et al. Cutmix: Regularization strategy to train strong classifiers with localizable features［C］// Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019:6023-6032.
［25］	LIU S， QI L， QIN H F， et al. Path aggregation network for in-stance segmentation［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018:8759-8768.
［26］	WOO S， PARK J， LEE J Y， et al. CBAM: Convolutional block attention module［C］// Proceedings of the European Conference on Computer Vision （ECCV）. 2018:3-19.
［27］	ZHU X K， LYU S C， WANG X， et al. TPH-YOLOv5s: Improved YOLOv5s based on transformer prediction head for object detection on drone-captured scenarios［C］// IEEE/CVF International Conference on Computer Vision Workshops （ICCVW）. 2021:2778-2788.
［28］	GE Z， LIU S T， WANG F， et al. Yolox: Exceeding YOLO series in 2021［J］. arXiv preprint arXiv:2107.08430， 2021.

[1]	张嘉琪, 徐啟蕾. 基于NAM-YOLO网络的苹果缺陷检测算法[J]. 计算机与现代化, 2023, 0(10): 53-58.
[2]	胡睿杰, 车逗. 红外小目标检测方法综述[J]. 计算机与现代化, 2023, 0(08): 79-86.
[3]	许叶彤, 耿信哲, 赵伟强, 张月, 宁海龙, 雷涛. 基于CNN-Transformer混合结构的遥感影像变化检测模型[J]. 计算机与现代化, 2023, 0(07): 79-85.
[4]	龚轩, 郭中华, 陈旺. 基于CA-TransUNet的遥感图像道路分割[J]. 计算机与现代化, 2023, 0(07): 112-118.
[5]	罗伟, 刘思远, 徐健祥, 董天培. 基于改进YOLOv5s的太阳能电池缺陷检测算法[J]. 计算机与现代化, 2023, 0(07): 119-126.
[6]	王艺成, 张国良, 张自杰, . 基于改进YOLOv5的小目标检测方法[J]. 计算机与现代化, 2023, 0(05): 100-105.
[7]	朱理清, 李祥, . 改进YOLOv5算法的遥感图像车辆检测[J]. 计算机与现代化, 2023, 0(05): 117-121.
[8]	汤浩威, 姚军财, 姚聪颖, 孙颖, 裴星懿, 宋春晓. 基于改进YOLOv5的输电线路多目标检测[J]. 计算机与现代化, 2023, 0(02): 78-82.
[9]	王洪义, 孔梅梅, 徐荣青. 基于改进YOLOV5的火焰检测算法[J]. 计算机与现代化, 2023, 0(01): 103-107.
[10]	焦新泉, 李睿康, 陈建军. 基于改进的MoCo的遥感图像目标检测[J]. 计算机与现代化, 2022, 0(12): 88-94.
[11]	王梦, 张鸿鑫, 刘庆华, 张东. 基于改进YOLOv5的幽门螺杆菌免疫印迹图像识别[J]. 计算机与现代化, 2022, 0(09): 78-84.
[12]	彭露露, 朱媛媛, 金文倩, 王笑梅. 基于改进YOLOv4的汽车钢铁零件表面缺陷检测[J]. 计算机与现代化, 2022, 0(09): 32-39.
[13]	谭金林, 范文童, 刘亚虎, 梁志锋, 王梁, 刘斌, 黄斌. 基于软硬件协同加速框架的遥感图像目标检测[J]. 计算机与现代化, 2022, 0(06): 109-115.
[14]	屈晓渊, 张永恒. 基于主动学习的遥感图像地块目标分类[J]. 计算机与现代化, 2021, 0(11): 50-55.
[15]	苏浩, 丁胜, 章超华, . 主动迁移学习的海上任意方向船只目标检测[J]. 计算机与现代化, 2021, 0(09): 21-30.