基于空间相关性增强的无人机检测算法

doi:10.3785/j.issn.1008-973X.2024.03.004

浙江大学学报(工学版)

2024, Vol. 58

Issue (3): 468-479 DOI: 10.3785/j.issn.1008-973X.2024.03.004

计算机技术

基于空间相关性增强的无人机检测算法

张会娟1,2(

),李坤鹏1,姬淼鑫1,*(

),刘振江1,刘建娟1,张弛1

1. 河南工业大学电气工程学院，河南郑州 450001
2. 北京理工大学自动化学院，北京 100081

UAV detection algorithm based on spatial correlation enhancement

Huijuan ZHANG1,2(

),Kunpeng LI1,Miaoxin JI1,*(

),Zhenjiang LIU1,Jianjuan LIU1,Chi ZHANG1

1. College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
2. School of Automation, Beijing Institute of Technology, Beijing 100081, China

全文: PDF(3100 KB) HTML

摘要：

针对无人机(UAV)体积小、复杂背景下特征难以提取导致被误检和漏检的问题，提出基于自适应上采样和空间相关性增强的无人机小目标检测方法. 采用多尺度的空洞卷积获取重要的上下文信息，然后通过注意力特征融合模块抑制多尺度特征融合造成的信息冲突；采用亚像素卷积和双线性插值自适应融合的新上采样方式，融合更多无人机特征信息，同时平衡计算量；对深层特征图的空间局部特征和全局特征采用空间相关性增强策略，提高复杂背景下前景目标的敏感度，增强目标表达和抑制背景噪声. 在自制无人机数据集上进行消融实验和对比实验，与原始YOLOv5算法相比，本算法的mAP0.5和mAP0.5∶0.95分别提高了2.4%和2.7%，检测速度能够达到58.5帧/s；在VisDrone2019数据集上进行验证，本算法较YOLOv5算法的mAP0.5和mAP0.5∶0.95分别提高了4.6%和1.3%.

关键词： 无人机(UAV); 小目标检测; 特征融合; 自适应上采样; 空间相关性增强

Abstract:

A small target detection method for unmanned aerial vehicle (UAV) based on adaptive up-sampling and spatial correlation enhancement was proposed, to resolve the problem of false detection and missed detection caused by the small size of UAV and the difficulty of feature extraction under complex backgrounds. Firstly, the important contextual information was obtained by multi-scale dilated convolution, and then the attention feature fusion module was used to suppress the information conflict of multi-scale feature fusion; Secondly, a new up-sampling method of sub-pixel convolution and bilinear interpolation adaptive fusion was adopted to balance the computation and to fuse more UAV feature information; Finally, spatial correlation enhancement strategies for local and global spatial features were performed on deep features to improve the sensitivity of foreground targets in complex backgrounds and enhance target expression to suppress background noise. Ablation experiments and comparative experiments were implemented on the self-made UAV dataset. The mAP0.5 and mAP0.5:0.95 of the proposed algorithm were increased by 2.4% and 2.7% respectively, compared with those of the original YOLOv5 algorithm. Furthermore, the detection speed was able to achieve 58.5 frames per second. The performance of the proposed algorithm was also verified on the VisDrone2019 dataset, and its mAP0.5 and mAP0.5:0.95 were respectively higher than those of the YOLOv5 algorithm by 4.6% and 1.3%.

Key words: unmanned aerial vehicle (UAV) small target detection feature fusion adaptive up-sampling spatial correlation enhancement

收稿日期: 2023-07-14 出版日期: 2024-03-05

CLC:

TP 391

基金资助: 国家资助博士后研究人员计划（GZC20233408）；国家自然科学基金资助项目（62201199）；河南省科技攻关项目（232102320037）；河南工业大学创新基金支持计划专项（2021ZKCJ07）；河南省专业学位研究生精品教学案例项目（YJS2022AL043）.

通讯作者: 姬淼鑫 E-mail: zhanghjqy@163.com;jimiaoxin@haut.edu.cn

作者简介: 张会娟（1988—），女，副教授，从事目标检测与跟踪、磁轴承控制、导航制导与控制研究. orcid.org/0000-0002-7165-2714. E-mail：zhanghjqy@163.com

	服务
	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	作者相关文章
	张会娟
	李坤鹏
	姬淼鑫
	刘振江
	刘建娟
	张弛

引用本文:

张会娟,李坤鹏,姬淼鑫,刘振江,刘建娟,张弛. 基于空间相关性增强的无人机检测算法[J]. 浙江大学学报(工学版), 2024, 58(3): 468-479.

Huijuan ZHANG,Kunpeng LI,Miaoxin JI,Zhenjiang LIU,Jianjuan LIU,Chi ZHANG. UAV detection algorithm based on spatial correlation enhancement. Journal of ZheJiang University (Engineering Science), 2024, 58(3): 468-479.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2024.03.004 或 https://www.zjujournals.com/eng/CN/Y2024/V58/I3/468

图 1 改进后的YOLOv5网络结构

图 2 多尺度上下文信息和注意力特征融合增强模块

图 3 改进上采样模块

表 1 特征融合方式对比实验

图 4 空间相关性增强

图 5 自制无人机数据集的样本图像

表 2 不同尺度下的边界框统计

图 6 自制无人机数据集中无人机标签大小的分布图

图 7 VisDrone 2019数据集中训练集和验证集中每个类别的标签分布情况

表 3 自制无人机数据集上的消融实验结果

图 8 训练过程中mAP0.5的变化过程

图 9 YOLOv5和本研究所提算法的检测结果对比

表 4 不同检测方法在自制无人机数据集上的实验结果

表 5 VisDrone2019数据集上的检测结果

表 6 VisDrone2019数据集上各个类别的mAP0.5

图 10 挑战性测试集检测结果

1	BALESTRIERI E, DAPONTE P, DE VITO L, et al Sensors and measurements for UAV safety: an overview[J]. Sensors, 2021, 21 (24): 8253 doi: 10.3390/s21248253
2	LIU Y, SUN P, WERGELES N, et al A survey and performance evaluation of deep learning methods for small object detection[J]. Expert Systems with Applications, 2021, 172: 114602 doi: 10.1016/j.eswa.2021.114602
3	KOUSHIK J. Understanding convolutional neural networks [EB/OL]. (2016-05-30). https://arxiv.org/abs/1605.09081.
4	韩俊, 袁小平, 王准, 等基于YOLOv5s的无人机密集小目标检测算法[J]. 浙江大学学报:工学版, 2023, 57 (6): 1224- 1233 HAN Jun, YUAN Xiaoping, WANG Zhun, et al UAV dense small target detection algorithm based on YOLOv5s[J]. Journal of Zhejiang University: Engineering Science, 2023, 57 (6): 1224- 1233
5	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 . Columbus: IEEE, 2014: 580−587.
6	GIRSHICK R. Fast R-CNN [C]// Proceedings of the IEEE International Conference on Computer Vision . Santiago: IEEE, 2015: 1440−1448.
7	REN S Q, HE K M, GIRSHICK R, et al Faster R-CNN: towards real-time object detection with region proposal networks[J]. Advances in Neural Information Processing Systems, 2016, 39 (6): 1137- 1149
8	HE K, GKIOXARI G, DOLLAR P, et al. Mask R-CNN [C]// Proceedings of the IEEE International Conference on Computer Vision . Venice: IEEE, 2017: 2961–2969.
9	张艳, 孙晶雪, 孙叶美, 等基于分割注意力与线性变换的轻量化目标检测[J]. 浙江大学学报:工学版, 2023, 57 (6): 1195- 1204 ZHANG Yan, SUN Jingxue, SUN Yemei, et al Lightweight object detection based on split attention and linear transformation[J]. Journal of Zhejiang University: Engineering Science, 2023, 57 (6): 1195- 1204
10	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 . Las Vegas: IEEE, 2016: 779–788.
11	LIU W, ANGUELOY D, ERHAN D, et al. SSD: single shot multibox detector [C]// Proceedings of the 14th European Conference on Computer Vision . Berlin: ECCV, 2016: 21−37.
12	WANG Q, QIAN Y, HU Y, et al M2YOLOF: based on effective receptive fields and multiple-in-single-out encoder for object detection[J]. Expert Systems with Applications, 2023, 213: 118928 doi: 10.1016/j.eswa.2022.118928
13	PENG C, ZHU M, REN H G, et al Small object detection method based on weighted feature fusion and CSMA attention module[J]. Electronics, 2022, 11 (16): 2546 doi: 10.3390/electronics11162546
14	MIN K, LEE G H, LEE S W Attentional feature pyramid network for small object detection[J]. Neural Networks, 2022, 155: 439- 450 doi: 10.1016/j.neunet.2022.08.029
15	JU M R, LUO J N, ZHANG P P, et al A simple and efficient network for small target detection[J]. IEEE Access, 2019, 7: 85771- 85781 doi: 10.1109/ACCESS.2019.2924960
16	DENG C F, WANG M M, LIU L, et al Extended feature pyramid network for small object detection[J]. IEEE Transactions on Multimedia, 2021, 24: 1968- 1979
17	HE X W, CHENG R, ZHENG Z L, et al Small object detection in traffic scenes based on YOLO-MXANet[J]. Sensors, 2021, 21 (21): 7422 doi: 10.3390/s21217422
18	JI S J, LING Q H, HAN F An improved algorithm for small object detection based on YOLOv4 and multi-scale contextual information[J]. Computers and Electrical Engineering, 2023, 105: 108490 doi: 10.1016/j.compeleceng.2022.108490
19	张娜, 戚旭磊, 包晓安, 等基于优化预测定位的单阶段目标检测算法[J]. 浙江大学学报:工学版, 2022, 56 (4): 783- 794 ZHANG Na, QI Xulei, BAO Xiaoan, et al Single-stage object detection algorithm based on optimizing position prediction[J]. Journal of Zhejiang University: Engineering Science, 2022, 56 (4): 783- 794
20	谢誉, 包梓群, 张娜, 等基于特征优化与深层次融合的目标检测算法[J]. 浙江大学学报:工学版, 2022, 56 (12): 2403- 2415 XIE Yu, BAO Ziqun, ZHANG Na, et al Object detection algorithm based on feature enhancement and deep fusion[J]. Journal of Zhejiang University: Engineering Science, 2022, 56 (12): 2403- 2415
21	BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection [EB/OL]. (2020-04-23). https://arxiv.org/abs/2004.10934v1.
22	LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Salt Lake City: IEEE, 2018: 8759-8768.
23	LIU H Y, SUN F Q, GU J, et al Sf-YOLOv5: a lightweight small object detection algorithm based on improved feature fusion mode[J]. Sensors, 2022, 22 (15): 5817 doi: 10.3390/s22155817
24	张云佐, 郭威, 蔡昭权, 等联合多尺度与注意力机制的遥感图像目标检测[J]. 浙江大学学报:工学版, 2022, 56 (11): 2215- 2223 ZHANG Yunzuo, GUO Wei, CAI Zhaoquan, et al Remote sensing image target detection combining multi-scale and attention mechanism[J]. Journal of Zhejiang University:Engineering Science, 2022, 56 (11): 2215- 2223
25	KIM M, KIM H, SUNG J, et al High-resolution processing and sigmoid fusion modules for efficient detection of small objects in an embedded system[J]. Scientific Reports, 2023, 13 (1): 244 doi: 10.1038/s41598-022-27189-5
26	CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFS [EB/OL]. (2014-12-22). https://arxiv.org/abs/1412.7062.
27	ZHAN W, SUN C F, WANG M C, et al An improved YOLOv5 real-time detection method for small objects captured by UAV[J]. Soft Computing, 2022, 26: 361- 373 doi: 10.1007/s00500-021-06407-8
28	WOO S, PARK J, LEE J Y, et al. Cbam: convolutional block attention module [C]// Proceedings of the European Conference on Computer Vision . Munich: ECCV. 2018: 3−19.
29	DU D W, ZHU P F, WEN L Y, et al. VisDrone-DET2019: the vision meets drone object detection in image challenge results [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops . Seoul: IEEE, 2019.
30	ZHANG S F, WEN L Y, BIAN X, et al. Single-shot refinement neural network for object detection [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Salt Lake City: IEEE, 2018: 4203−4212.
31	LIU S H, ZHA J L, SUN J, et al. EdgeYOLO: an edge-real-time object detector [EB/OL]. [2023-02-15]. https://arxiv.org/abs/2302.07483.
32	WANG C Y, BOCHKOVSKIY A, LIAO H Y M. Scaled-YOLOv4: scaling cross stage partial network [C]// Proceedings of the IEEE/cvf Conference on Computer Vision and Pattern Recognition . Nashville: IEEE, 2021: 13029−13038.
33	CUI L S, MA R, LV P, et al MDSSD: multi-scale deconvolutional single shot detector for small objects[J]. Science China Information Sciences, 2020, 63: 120113 doi: 10.1007/s11432-019-2723-1

[1]	韩俊,袁小平,王准,陈烨. 基于YOLOv5s的无人机密集小目标检测算法[J]. 浙江大学学报(工学版), 2023, 57(6): 1224-1233.
[2]	张剑钊,郭继昌,汪昱东. 基于融合逆透射率图的水下图像增强算法[J]. 浙江大学学报(工学版), 2023, 57(5): 921-929.
[3]	艾青林,崔景瑞,吕冰海,童桐. 基于损伤区域融合变换的轴承鼓形滚子表面损伤检测方法[J]. 浙江大学学报(工学版), 2023, 57(5): 1009-1020.
[4]	杨帆,宁博,李怀清,周新,李冠宇. 基于语义增强特征融合的多模态图像检索模型[J]. 浙江大学学报(工学版), 2023, 57(2): 252-258.
[5]	陈巧红,孙佳锦,漏杨波,方志坚. 基于多任务学习与层叠 Transformer 的多模态情感分析模型[J]. 浙江大学学报(工学版), 2023, 57(12): 2421-2429.
[6]	侯小虎,贾晓芬,赵佰亭. 眼底病变OCT图像的轻量化识别算法[J]. 浙江大学学报(工学版), 2023, 57(12): 2448-2455.
[7]	廖嘉文,庞彦伟,聂晶,孙汉卿,曹家乐. 基于特征融合和一致性损失的双目低光照增强[J]. 浙江大学学报(工学版), 2023, 57(12): 2456-2466.
[8]	艾青林,杨佳豪,崔景瑞. 基于自适应增殖数据增强与全局特征融合的小目标行人检测[J]. 浙江大学学报(工学版), 2023, 57(10): 1933-1944.
[9]	莫建文,李晋,蔡晓东,陈锦威. 基于门控特征融合与中心损失的目标识别[J]. 浙江大学学报(工学版), 2023, 57(10): 2011-2017.
[10]	王思鹏,杜昌平,宋广华,郑耀. 基于改进MSCKF的无人机室内定位方法[J]. 浙江大学学报(工学版), 2022, 56(4): 711-717.
[11]	张娜,戚旭磊,包晓安,吴彪,涂小妹,金瑜婷. 基于优化预测定位的单阶段目标检测算法[J]. 浙江大学学报(工学版), 2022, 56(4): 783-794.
[12]	黄新宇,游帆,张沛,张昭,张柏礼,吕建华,徐立臻. 基于多分类及特征融合的静默活体检测算法[J]. 浙江大学学报(工学版), 2022, 56(2): 263-270.
[13]	于楠晶,范晓飚,邓天民,冒国韬. 基于多头自注意力的复杂背景船舶检测算法[J]. 浙江大学学报(工学版), 2022, 56(12): 2392-2402.
[14]	张云佐,郭威,蔡昭权,李文博. 联合多尺度与注意力机制的遥感图像目标检测[J]. 浙江大学学报(工学版), 2022, 56(11): 2215-2223.
[15]	李明,段立娟,王文健,恩擎. 基于显著稀疏强关联的脑功能连接分类方法[J]. 浙江大学学报(工学版), 2022, 56(11): 2232-2240.

Viewed

Full text

Abstract

Cited

Shared

Discussed