基于改进YOLOv5的遥感图像目标检测

doi:10.19678/j.issn.1000-3428.0067790

摘要/Abstract

摘要：

目前目标检测技术虽然已经趋于成熟, 但是对遥感图像的检测仍存在不少挑战。针对遥感图像的背景复杂、目标尺度差异大、目标方向任意等特点造成目标检测精度低下的问题, 提出一种基于改进YOLOv5的遥感图像目标检测算法。首先, 构建一种联合注意力的多尺度特征增强网络, 充分融合高低层特征, 使特征层具有语义信息的同时包含丰富的细节信息, 并在融合过程中利用设计的特征聚焦模块帮助模型选择关键特征, 抑制无关信息。其次, 使用感受野模块(RFB)对融合后的特征图进行更新, 扩大特征图的感受野, 减少特征信息损失。最后, 对目标增加旋转角度, 并采用圆形平滑标签将回归问题转化成分类问题, 提高遥感目标定位的准确性。在用于航拍图像目标检测的大规模数据集(DOTA)上的实验结果表明, 与YOLOv5算法相比, 所提算法的交并比(IoU)为0.5和0.5~0.95时的平均精度均值(mAP@0.5和mAP@0.5∶ 0.95)分别提高了7.3和3.3个百分点, 能够明显提高复杂背景下遥感图像目标的检测精度, 并改善对遥感目标的漏检和误检情况。

关键词: 目标检测, 遥感图像, 特征融合, 感受野模块, 圆形平滑标签

Abstract:

Although target detection technology has advanced, many challenges still exist in the detection of remote-sensing images. An improved YOLOv5-based remote-sensing image target detection algorithm is proposed to address the issues of low target detection accuracy caused by complex backgrounds, large target scale differences, and arbitrary target orientation in remote-sensing images. First, a joint multiscale feature enhancement network with attention is constructed to fully fuse high-level and low-level features such that the feature layers contain semantic and rich detailed information. During the fusion process, the designed feature focusing module is used to help the model select key features and suppress irrelevant information. Second, a Receptive Field Block(RFB) is used to update the fused feature map and expand the receptive field of the feature map to reduce feature information loss. Finally, by adding rotation angles to the targets and using circular smooth labels to transform the regression problem into a classification problem, the accuracy of remote-sensing target localization is improved. The experimental results on the a large-scale Dataset for Object deTection in Aerial images(DOTA) show that compared with the YOLOv5 algorithm, the mean Average Precision(mAP) when the Intersection over Union (IoU) values of the proposed algorithm are 0.5 and 0.5-0.95 (mAP@0.5 and mAP@0.5∶0.95) increase by 7.3 and 3.3 percentage points, respectively. This can significantly improve the detection accuracy of remote-sensing image targets in a complex background and improve the missing and false detection of remote-sensing targets.

Key words: target detection, remote-sensing image, feature fusion, Receptive Field Block(RFB), circular smooth label

崔丽群, 曹华维. 基于改进YOLOv5的遥感图像目标检测[J]. 计算机工程, 2024, 50(4): 228-236.

Liqun CUI, Huawei CAO. Target Detection of Remote-Sensing Images Based on Improved YOLOv5[J]. Computer Engineering, 2024, 50(4): 228-236.

http://www.ecice06.com/CN/Y2024/V50/I4/228

图/表 12

图1 YOLOv5算法特征融合网络

Fig.1 Feature fusion network of YOLOv5 algorithm

图2 联合注意力的多尺度特征增强网络

Fig.2 Joint multiscale feature-enhanced network with attention

图3 感受野模块结构

Fig.3 Structure of receptive field block

图4 引入旋转角度的检测框

Fig.4 Detection box with introduced rotation angle

图5 圆形平滑标签

Fig.5 Circular smooth label

图6 所提算法网络结构

Fig.6 Network structure of the proposed algorithm

图7 YOLOv5与所提算法检测效果可视化对比

Fig.7 Visual comparison of detection effect between YOLOv5 and the proposed algorithm

参考文献 26

1	逄涛, 张学敏, 姚亚洲, 等. 基于特征增强的光学遥感图像建筑物变化检测. 计算机工程, 2023, 49(4): 182- 187. URL
	PANG T, ZHANG X M, YAO Y Z, et al. Optical remote sensing image building change detection based on feature enhancement. Computer Engineering, 2023, 49(4): 182- 187. URL
2	朱淑鑫, 周子俊, 顾兴健, 等. 基于RCF网络的遥感图像场景分类研究. 激光与光电子学进展, 2021, 58(14): 1401001. URL
	ZHU S X, ZHOU Z J, GU X J, et al. Scene classification of remote sensing images based on RCF network. Laser & Optoelectronics Progress, 2021, 58(14): 1401001. URL
3	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington D. C. , USA: IEEE Press, 2016: 779-788.
4	REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington D. C. , USA: IEEE Press, 2017: 6517-6525.
5	REDMON J, FARHADI A. YOLOv3: an incremental improvement[C]//Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition. Washington D. C. , USA: IEEE Press, 2018: 1-6.
6	BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. [2023-05-11]. https://arxiv.org/abs/2004.10934.
7	GIRSHICK R. Fast R-CNN[C]//Proceedings of IEEE International Conference on Computer Vision. Washington D. C. , USA: IEEE Press, 2015: 1440-1448.
8	REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137- 1149. doi: 10.1109/TPAMI.2016.2577031
9	宋忠浩, 谷雨, 陈旭, 等. 基于加权策略的高分辨率遥感图像目标检测. 计算机工程与应用, 2021, 57(13): 199- 206. doi: 10.3778/j.issn.1002-8331.2004-0291
	SONG Z H, GU Y, CHEN X, et al. Target detection in high-resolution remote sensing image based on weighted strategy. Computer Engineering and Applications, 2021, 57(13): 199- 206. doi: 10.3778/j.issn.1002-8331.2004-0291
10	LUO S, YU J, XI Y, et al. Aircraft target detection in remote sensing images based on improved YOLOv5. IEEE Access, 2022, 10, 5184- 5192. doi: 10.1109/ACCESS.2022.3140876
11	WANG X, HE N, HONG C, et al. Improved YOLOX-X based UAV aerial photography object detection algorithm. Image and Vision Computing, 2023, 135, 104697. doi: 10.1016/j.imavis.2023.104697
12	王浩桐, 郭中华. 锚框策略匹配的SSD飞机遥感图像目标检测. 计算机科学与探索, 2022, 16(11): 2596. doi: 10.3778/j.issn.1673-9418.2105108
	WANG H T, GUO Z H. Target detection in remote sensing images of SSD aircraft based on anchor frame strategy matching. Journal of Computer Science and Exploration, 2022, 16(11): 2596. doi: 10.3778/j.issn.1673-9418.2105108
13	王道累, 杜文斌, 刘易腾, 等. 基于密集连接与特征增强的遥感图像检测. 计算机工程, 2022, 48(6): 251-256, 262. URL
	WANG D L, DU W B, LIU Y T, et al. Remote sensing images detection based on dense connection and feature enhancement. Computer Engineering, 2022, 48(6): 251-256, 262. URL
14	DING J, XUE N, LONG Y, et al. Learning RoI Transformer for oriented object detection in aerial images[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C. , USA: IEEE Press, 2019: 2849-2858.
15	YANG X, YANG J R, YAN J C, et al. SCRDet: towards more robust detection for small, cluttered and rotated objects[C]//Proceedings of IEEE/CVF International Conference on Computer Vision. Washington D. C. , USA: IEEE Press, 2019: 8232-8241.
16	LIN T Y, DOLLAR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington D. C. , USA: IEEE Press, 2017: 2117-2125.
17	LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C. , USA: IEEE Press, 2018: 8759-8768.
18	TAN M X, PANG R M, LE Q V. EfficientDet: scalable and efficient object detection[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C. , USA: IEEE Press, 2020: 10781-10790.
19	WANG C Y, MARK LIAO H Y, WU Y H, et al. CSPNet: a new backbone that can enhance learning capability of CNN[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Washington D. C. , USA: IEEE Press, 2020: 1571-1580.
20	WANG Q L, WU B G, ZHU P F, et al. ECA-Net: efficient channel attention for deep convolutional neural networks[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C. , USA: IEEE Press, 2020: 11534-11542.
21	LIU S T, HUANG D, WANG Y H. Receptive field block net for accurate and fast object detection[C]//Proceedings of European Conference on Computer Vision. Berlin, Germany: Springer, 2018: 404-419.
22	YANG X, YAN J C. Arbitrary-oriented object detection with circular smooth label[C]//Proceedings of European Conference on Computer Vision. Berlin, Germany: Springer, 2020: 677-694.
23	XIA G S, BAI X, DING J, et al. DOTA: a large-scale dataset for object detection in aerial images[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C. , USA: IEEE Press, 2018: 3974-3983.
24	JIANG Y, ZHU X, WANG X, et al. R2CNN: rotational region CNN for orientation robust scene text detection[EB/OL]. [2023-05-11]. https://arxiv.org/abs/1706.09579.
25	WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[EB/OL]. [2023-05-11]. https://arxiv.org/abs/2207.02696.
26	WANG J W, DING J, GUO H W, et al. Mask OBB: a semantic attention-based mask oriented bounding box representation for multi-category object detection in aerial images. Remote Sensing, 2019, 11(24): 2930. doi: 10.3390/rs11242930

[1]	李振鲁, 黄威, 孙锴. 复杂环境下的轻量化道路目标识别算法研究[J]. 计算机工程, 2024, 50(4): 219-227.
[2]	安玉, 葛海波, 何文昊, 马赛, 程梦洋. 基于补偿注意力机制的Siamese网络跟踪算法[J]. 计算机工程, 2024, 50(4): 187-196.
[3]	赵继达, 甄国涌, 储成群. 基于YOLOv8的无人机图像目标检测算法[J]. 计算机工程, 2024, 50(4): 113-120.
[4]	刘彦红, 杨秋翔, 胡帅. 基于特征差异的多尺度特征融合去雾网络研究[J]. 计算机工程, 2024, 50(4): 247-257.
[5]	谢帅康, 熊风光, 朱新杰, 宋宁栋, 李文清, 王廷凤. 基于空间可变形Transformer的三维点云配准方法[J]. 计算机工程, 2024, 50(3): 224-232.
[6]	徐浩宸, 刘满华. 基于多层次自注意力网络的人脸特征点检测[J]. 计算机工程, 2024, 50(2): 239-246.
[7]	李伟健, 胡慧君. 基于潜在特征增强网络的视频描述生成方法[J]. 计算机工程, 2024, 50(2): 266-272.
[8]	周金涛, 高迪驹, 刘志全. 基于全景视觉的无人船水面障碍物检测方法[J]. 计算机工程, 2024, 50(2): 113-121.
[9]	圣文顺, 余熊峰, 林佳燕, 陈欣. 融合注意力与特征金字塔的小尺度目标检测算法[J]. 计算机工程, 2024, 50(1): 242-250.
[10]	祝冰艳, 陈志华, 盛斌. 基于感知增强Swin Transformer的遥感图像检测[J]. 计算机工程, 2024, 50(1): 216-223.
[11]	蒋心璐, 陈天恩, 王聪, 赵春江. 大田环境下的农业害虫图像小目标检测算法[J]. 计算机工程, 2024, 50(1): 232-241.
[12]	杨瑞君, 秦晋京, 程燕. 基于生成对抗网络的自然场景低照度增强模型[J]. 计算机工程, 2024, 50(1): 279-288.
[13]	范文卓, 吴涛, 许俊平, 李庆庆, 张建林, 李美惠, 魏宇星. 基于多分辨率特征融合的任意尺度图像超分辨率重建[J]. 计算机工程, 2023, 49(9): 217-225.
[14]	李嘉新, 侯进, 盛博莹, 周宇航. 基于改进YOLOv5的遥感小目标检测网络[J]. 计算机工程, 2023, 49(9): 256-264.
[15]	韩璐, 霍纬纲, 张永会, 刘涛. 基于多尺度特征融合与双注意力机制的多元时间序列预测[J]. 计算机工程, 2023, 49(9): 99-108.

选择文件类型/文献管理软件名称

选择包含的内容