Target Detection Algorithm of Remote Sensing Image Based on Improved YOLOv5

doi:10.3778/j.issn.1002-8331.2209-0119

Abstract

Abstract: Aiming at the problems of low target detection accuracy caused by high background complexity, multiple target sizes and too many small targets in remote sensing images, this paper proposes a target detection algorithm of remote sensing image based on improved YOLOv5. The channel-global attention mechanism（CGAM） is introduced into the backbone network to enhance the feature extraction ability of targets at different scales and to suppress the interference of redundant information. The dense upsampling convolution（DUC） module is introduced to expand the low resolution convolution feature maps, which can effectively enhance the fusion effect of different convolution feature maps. The improved algorithm is applied to the open remote sensing data set RSOD, and the average accuracy AP value of the improved YOLOv5 algorithm reaches 78.5%, which is 3.1?percentage points higher than that of the original algorithm. Experimental results show that the improved algorithm can effectively improve the accuracy of remote sensing image target detection.

Key words: remote sensing image, target detection, YOLOv5, channel attention mechanism, upsampling

摘要： 针对遥感图像中背景复杂度高、目标尺寸多样和小目标存在过多所导致的目标检测精度较低的问题，提出一种改进YOLOv5的遥感图像目标检测算法。该算法在主干网络引入通道-全局注意力机制（CGAM）以增强对不同尺度目标的特征提取能力和抑制冗余信息的干扰。引入密集上采样卷积（DUC）模块扩张低分辨率卷积特征图，有效增强不同卷积特征图的融合效果。将改进算法应用于公开遥感数据集RSOD中，改进YOLOv5算法平均精度AP值达到78.5%，较原算法提升了3.1个百分点。实验结果证明，改进后的算法能有效提高遥感图像目标检测精度。

关键词: 遥感图像, 目标检测, YOLOv5, 注意力机制, 上采样

LI Kunya, OU Ou, LIU Guangbin, YU Zefeng, LI Lin. Target Detection Algorithm of Remote Sensing Image Based on Improved YOLOv5[J]. Computer Engineering and Applications, 2023, 59(9): 207-214.

李坤亚, 欧鸥, 刘广滨, 于泽峰, 李林. 改进YOLOv5的遥感图像目标检测算法[J]. 计算机工程与应用, 2023, 59(9): 207-214.

References

[1] XUE Z，CHEN W，LI J.Enhancement and fusion of multi-scale feature maps for small object detection[C]//Proceedings of the 39th Chinese Control Conference，2020：7212-7217.
[2] 侯涛，蒋瑜.改进YOLOv4在遥感飞机目标检测中的应用研究[J].计算机工程与应用，2021，57（12）：224-230.
HOU T，JIANG Y.Application research of improved YOLOv4 in remote sensing aircraft target detection[J]. Computer Engineering and Applications，2021，57（12）： 224-230.
[3] GIRSHICK R.Fast R-CNN[EB/OL].（2015-09-27）[2021-12-06].https：//arxiv.org/pdf/1504.08083.pdf.
[4] REN S Q，HE K M，GRISHICK R，et al.Faster R-CNN：towards real-time object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems.Cambridge，MA：MIT Press，2015：21-37.
[5] LIU W，ANGUELOV D，ERHAN，D，et al.SSD：single shot multibox detector[C]//Proceedings of 14th European Conference on Computer Vision.Cham：Springer，2016：21-37.
[6] REDMON J，DIVVALA S，GIRSHICK R，et al.You only look once：unified，real-time object detection[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington DC：IEEE Computer Society，2016：779-788.
[7] REDMON J，FARHADI A.YOLO9000：better，faster，stronger[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington DC：IEEE Computer Society，2017：7263-7271.
[8] REDMON J，FARHADI A.YOLOv3：an incremental improvement[EB/OL].（2018-04-08）[2021-12-06].https：//arxiv.org/pdf/1804.02767.pdf.
[9] 汪鹏，辛雪静，王利琴，等.基于YOLOv3的光学遥感图像目标检测算法[J].激光与光电子学进展，2021，58（20）：509-517.
WANG P，XIN X J，WANG L Q，et al.Object detection algorithm of optical remote sensing images based on YOLOv3[J].Laser & Optoelectronics Progress，2021，58（20）：509-517.
[10] 刘芳，韩笑.基于多尺度深度学习的自适应航拍目标检测[J].航空学报，2022，43（5）：471-482.
LIU F，HAN X.Adaptive aerial object detection based on multi-scale deep learning[J].Acta Aeronauticaet Astronautica Sinica，2022，43（5）：471-482.
[11] 邢晋超，潘广贞.改进YOLOv5s的手语识别算法研究[J].计算机工程与应用，2022，58（16）：194-203.
XING J C，PAN G Z.Research on improved YOLOv5s sign language recognition algorithm[J].Computer Engineering and Applications，2022，58（16）：194-203.
[12] LIN T Y，DOLLáR 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.
[13] LIU S，QI L，QIN H F，et al.Path aggregation network for instance segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition（CVPR），2018：8759-8768.
[14] 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.
[15] YU F，KOLTUN V.Multi-scale context aggregation by dilated convolutions[J].arXiv：1511.07122，2015.
[16] BUADES A，COLL B，MOREL J M.Non-local means denoising[J].Image Processing on Line，2011，1：208-212.
[17] 王森，杨克俭.基于双线性插值的图像缩放算法的研究与实现[J].自动化技术与应用，2008，27（7）：44-45.
WANG S，YANG K J.An image scaling algorithm based on bilinear interpolation with VC++[J].Automation Technique and Application，2008，27（7）：44-45.
[18] ZEILER M D，KRISHNAN D，TAYLOR G W，et al.Deconvolutional networks[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition，2010：2528-2535.
[19] WANG P，CHEN P，YUAN Y，et al.Understanding convolution for semantic segmentation[C]//2018 IEEE Winter Conference on Applications of Computer Vision（WACV），2018：1451-1460.
[20] SHI W，CABALLERO J，HUSZáR F，et al.Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：1874-1883.