Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (3): 891-901.doi: 10.13229/j.cnki.jdxbgxb20221288

Previous Articles    

Anti⁃unmanned aerial vehicle system object detection algorithm under complex background

Shan XUE1,2(),Ya-liang ZHANG1,Qiong-ying LYU1,Guo-hua CAO2   

  1. 1.College of Mechanical and Electrical Engineering，Changchun University of Science and Technology，Changchun 130022，China
    2.Chongqing Research Institute，Changchun University of Science and Technology，Chongqing 401135，China
  • Received:2022-10-01 Online:2023-03-01 Published:2023-03-29

RICH HTML

 17   

PDF (PC)

317

Abstract:

Aiming at the problem that it is difficult to detect UAV in real time in public safety areas with complex flight backgrounds such as parks and playgrounds， the YOLOv5-Unmanned aerial vehicle object detection algorithm of anti-unmanned aerial vehicle system based on attention mechanism and the fusion of scale adaptive features is proposed. Firstly， the unmanned aerial vehicle data set is built by using fusion of selfie images and public data set DUT-Anti-Unmanned aerial vehicle. Secondly， the anchors is redesigned by using k-means method. Thirdly， the scale adaptive feature fusion module is designed. Then， the CIoU loss function is used as the positioning loss function of the YOLOv5s algorithm. Finally， the coordinate attention module is introduced in the backbone network to guide the network to pay attention to the channel and spatial position information of the unmanned aerial vehicle. The improved algorithm and the baseline algorithm are compared with the established data set， and the experimental results showed that the improved algorithm improved by 6.1%， 5.8%， and 5.2% in terms of precision rate， recall rate， and the average accuracy （mAP@0.5）， and the detection speed was 39 frame/s. On the dataset of this paper， compared with the YOLOv5m， YOLOv5l， and YOLOX object detection algorithms， the average accuracy （mAP@0.5） is 4.4%， 3.6%， and 1.3% higher than the comparison algorithms， indicating the effectiveness of the improved algorithm. A comparative experiment was conducted on the public data set VisDrone2019， the average accuracy （mAP@0.5） of the improved algorithm YOLOv5-Unmanned aerial vehicle for all categories is higher than the original algorithm YOLOv5s， and the detection effect of small objects in complex backgrounds is better， respectively.

Key words: computer application technology, anti-unmanned aerial vehicle system, object detection, YOLOv5s algorithm, complex background

CLC Number: 

  • TP391.4

Fig.1

Collect UAV image data"

Fig.2

Fusion algorithm chart"

Fig.3

DUT-Anti-UAV data set"

Fig.4

Network chart of YOLOv5-UAV"

Fig.5

Visualization of the dataset's ground truth box"

Fig.6

Schematic diagram of the COCO's anchors"

Fig.7

Schematic diagram of reclustering generates anchors"

Fig.8

Schematic diagram of the structure ofthe W-C module"

Fig.9

Schematic diagram of the structure of the MAW-C module"

Fig.10

Schematic diagram of loss prediction frame and real frame of CIoU_Loss"

Fig.11

Schematic diagram of coordinate attention"

Table 1

Self-built UAV image data distribution table"

无人机类型数量
DJI Mavic 21560
DJI Mavic Air1430
随机融合图片2305

Table 2

Experimental hyperparameters"

超参数数值
初始学习率0.01
终止学习率0.1
热身训练次数5
热身训练动量0.8
批量大小16
迭代数300
权重衰减系数0.0005

Table 3

Algorithm performance comparison before and after improving anchors"

算法PRmAP@0.5检测速度/（帧·s-1）
YOLOv5s85.486.582.631
YOLOv5s-Anchors86.788.583.533

Table 4

Algorithm performance comparison after improving feature fusion"

算法PRmAP@0.5检测速度/（帧·s-1）
YOLOv5s85.486.582.631
YOLOv5s-New-Concat89.388.986.238

Fig.12

Improved UAV CAM visualization map before and after feature fusion"

Table 5

Algorithm performance comparison before and after improving loss function"

算法PRmAP@0.5检测速度/（帧·s-1）
YOLOv5s85.486.582.631
YOLOv5s-CIoU_Loss86.587.183.736

Table 6

Algorithm performance comparison before and after adding coordinate attention"

算法PRmAP@0.5检测速度/（帧·s-1）
YOLOv5s85.486.582.631
YOLOv5s-CA-Net87.687.885.137

Fig.13

UAV CAM visualization before and after adding coordinate attention"

Table 7

Performance comparison before and after improving algorithm"

算法PRmAP@0.5检测速度/（帧·s-1）
YOLOv5s85.486.582.631
YOLOv5-UAV91.592.387.839

Fig.14

YOLOv5-UAV detection result"

Table 8

Comparison of ablation experiments"

算法PRmAP@0.5检测速度/（帧·s-1）
YOLOv5s85.486.582.631
A+N88.288.684.334
A+CI86.888.885.936
A+CA86.287.185.137
A+N+CI89.390.686.336
A+N+CA90.289.587.137
A+CI+CA89.989.686.938
N+CI87.388.685.937
N+CA88.689.386.235
N+CI+CA89.587.387.339
CI+CA87.688.586.638

Table 9

Comparison with other object detection algorithms"

算法mAP@0.5/%检测速度/（帧·s-1）
YOLOv5m83.430
YOLOv5l84.228
YOLOv5x88.325
YOLOX86.533
YOLOv5-UAV87.839

Fig.15

Chart of detection results for different algorithms"

Table 10

Performance of each category of VisDrone 2019 data set with mAP@0.5"

数据集算法
YOLOv5sYOLOv5-UAV
All0.3570.413
pedestrian0.4630.558
people0.3590.429
bicycle0.1260.188
car0.7670.829
van0.3890.469
truck0.2950.341
tricycle0.1990.226
awning-tricycle0.1150.139
bus0.4370.485
motor0.4210.467
1 高明华, 杨璨. 基于改进卷积神经网络的交通目标检测方法[J]. 吉林大学学报:工学版, 2022, 52(6), 1353-1361.
Gao Ming-hua, Yang Can. Traffic target detection method based on improved convolution neural network[J]. Journal of Jilin University (Engineering and Technology Edition), 2022, 52(6): 1353-1361.
2 薛珊, 卫立炜, 顾宸瑜, 等. 采用混合域注意力机制的无人机识别方法[J]. 西安交通大学学报, 2022, 56(10): 141-150.
Xue Shan, Wei Li-wei, Gu Chen-yu, et al. Drone identification method based on mixed domain attention mechanism[J]. Journal of Xi'an Jiaotong University, 2022, 56(10): 141-150.
3 薛珊, 陈宇超, 吕琼莹, 等. 基于坐标注意力机制融合的反无人机系统图像识别方法[J]. 红外与激光工程, 2022, 51(9): 1-11.
Xue Shan, Chen Yu-chao, Lv Qiong-ying, et al. Image recognition method of anti drone system based on coordinate attention mechanism[J]. Infrared and Laser Engineering, 2022, 51(9): 1-11.
4 朱孟真, 陈霞, 刘旭, 等. 战术激光武器反无人机发展现状和关键技术分析[J]. 红外与激光工程, 2021, 50(7): 1-13.
Zhu Meng-zhen, Chen Xia, Liu Xu, et al. Situation and key technology of tactical laser anti-UAV[J]. Infrared and Laser Engineering, 2021, 50(7): 1-13.
5 朱威, 王立凯, 靳作宝, 等. 引入注意力机制的轻量级小目标检测网络[J]. 光学精密工程, 2022, 30(8): 998-1010.
Zhu Wei, Wang Li-kai, Jin Zuo-bao, et al. Lightweight small object detection network with attention mechanism[J]. Optics and Precision Engineering, 2022, 30(8): 998-1010.
6 严春满, 王钺. 基于特征增强的SAR图像舰船小目标检测算法[J]. 控制与决策, 2023, 38(1): 239-247.
Yan Chun-man, Wang Yue. A ship small target detection algorithm based on feature enhancement in SAR image[J]. Control and Decision, 2023, 38(1): 239-247.
7 陈欣, 万敏杰, 马超, 等. 采用多尺度特征融合SSD的遥感图像小目标检测[J]. 光学精密工程, 2021, 29(11): 2672-2682.
Chen Xin, Wan Min-jie, Ma Chao, et al. Recognition of small targets in remote sensing image using multi-scale feature fusion-based shot multi-box detector[J]. Optics and Precision Engineering, 2021, 29(11): 2672-2682.
8 陈海燕, 甄霞军, 赵涛涛. 一种通道-空间注意力机制特征融合的小目标检测模型[J/OL]. [2022-09-23].
9 苏佳,贾欣雨,侯卫民. 基于YOLO-J的PCB缺陷检测算法[J/OL]. [2022-09-25].
10 邵盼愉. 基于视觉的无人机入侵检测与跟踪系统设计与实现[D]. 杭州: 浙江大学控制科学与工程学院,2018.
Shao Pan-yu. Design and implementation of vision based drone intrusion detection and tracking system [D]. Hangzhou: College of Control Science and Engineering, Zhejiang University, 2018.
11 王晓. 视频中无人机的实时检测与跟踪算法研究[D]. 哈尔滨: 哈尔滨工程大学信息与通信工程学院,2019.
Wang Xiao. Research on real-time detection and tracking algorithm of UAV in video[D]. Harbin: College of Information and Communication Engineering, Harbin Engineering University, 2019.
12 王子哲. 基于视频的无人机目标检测算法研究[D]. 郑州: 郑州大学信息工程学院, 2019.
Wang Zi-zhe. Research on UAV target detection algorithm based on video[D]. Zhengzhou: College of Information Engineering, Zhengzhou University, 2019.
13 崔艳鹏, 王元皓, 胡建伟. 一种改进YOLOv3的动态小目标检测方法[J]. 西安电子科技大学学报, 2020, 47(3): 1-7.
Cui Yan-peng, Wang Yuan-hao, Hu Jian-wei. Detection method for a dynamic small target using the improved YOLOv3[J]. Journal of Xidian University, 2020, 47(3): 1-7.
14 吴曼佳. 基于视觉的多尺度低空小型无人机检测系统[D]. 杭州: 浙江大学控制科学与工程学院, 2020.
Wu Man-jia. Multi-scale target detection system for low-altitude small UAV based on vision[D]. Hangzhou: College of Control Science and Engineering, Zhejiang University, 2020.
15 陶磊, 洪韬, 钞旭. 基于YOLOv3的无人机识别与定位追踪[J]. 工程科学学报, 2020, 42(4): 463-468.
Tao Lei, Hong Tao, Chao Xu. Drone identification and location tracking based on YOLOv3[J]. Chinese Journal of Engineering, 2020, 42(4): 463-468.
16 Zhao J, Zhang J H, Li D D, et al. Vision-based anti-UAV detection and tracking[EB/OL]. [2022-09-28].
17 He K, Zhang X Y, Ren S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916.
18 Kalayeh M M, Shah M. Training faster by separating modes of variation in batch-normalized models[J]. IEEE Trans Pattern Anal Mach Intell, 2020, 42(6): 1483-1520.
19 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, USA, 2018: 8759-8768.
20 Tan M X, Pang R M, Le Q V. Scalable and efficient object detection[EB/OL]. [2022-09-28].
21 Liu S T, Huang D, Wang Y H. Learning spatial fusion for single-shot object detection[EB/OL]. [2022-09-29].
22 Rezatofighi H, Tsoi N, Gwak J, et al. A metric and a loss for bounding box regression[EB/OL]. [2022-09-29].
23 Zheng Z H, Wang P, Liu W, et al. Faster and better learning for bounding box regression[EB/OL]. [2022-09-29].
24 Hou Q B, Zhou D Q, Feng J S. Coordinate attention for efficient mobile network design[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Nashville, 2021: 13713-13722.
25 Zhou B, Khosla A, Lapedriza A, et al. Learning Deep features for discriminative localization[EB/OL].[2022-10-01].
26 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 and Machine Intelligence, 2016, 39(6): 1137-1149.
27 Ge Z, Liu S T, Wang F, et al. YOLOX: exceeding YOLO series in 2021[EB/OL]. [2022-09-30].
28 Du D W, Zhu P F, Wen L Y, et al. VisDrone-Det2019: the vision meets drone object detection in image challenge resulted[C]∥Proceedings of the IEEE Conference on Computer Vision Workshop, Seoul, South Korea, 2019: 213-226.
[1] Bo TAO,Fu-wu YAN,Zhi-shuai YIN,Dong-mei WU. 3D object detection based on high⁃precision map enhancement [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 802-809.
[2] Fu-heng QU,Tian-yu DING,Yang LU,Yong YANG,Ya-ting HU. Fast image codeword search algorithm based on neighborhood similarity [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1865-1871.
[3] Tian BAI,Ming-wei XU,Si-ming LIU,Ji-an ZHANG,Zhe WANG. Dispute focus identification of pleading text based on deep neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1872-1880.
[4] Gui-he QIN,Jun-feng HUANG,Ming-hui SUN. Text input based on two⁃handed keyboard in virtual environment [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1881-1888.
[5] Shi-min FANG. Multiple source data selective integration algorithm based on frequent pattern tree [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(4): 885-890.
[6] Ming LIU,Yu-hang YANG,Song-lin ZOU,Zhi-cheng XIAO,Yong-gang ZHANG. Application of enhanced edge detection image algorithm in multi-book recognition [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(4): 891-896.
[7] Xiang-jiu CHE,He-yuan CHEN. Muti⁃Object dishes detection algorithm based on improved YOLOv4 [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2662-2668.
[8] Sheng-sheng WANG,Chen-xu LI,Xiang-yu WANG,Zhi-lin YAO,Yi-shen LIU,Jia-qian WU,Qing-ran YANG. Brain tumor image classification based on improved residual capsule network and sparrow search [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2653-2661.
[9] You QU,Wen-hui LI. Single-stage rotated object detection network based on anchor transformation [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(1): 162-173.
[10] Sheng-sheng WANG,Jing-yu CHEN,Yi-nan LU. COVID⁃19 chest CT image segmentation based on federated learning and blockchain [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2164-2173.
[11] Hong-wei ZHAO,Zi-jian ZHANG,Jiao LI,Yuan ZHANG,Huang-shui HU,Xue-bai ZANG. Bi⁃direction segmented anti⁃collision algorithm based on query tree [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1830-1837.
[12] Jie CAO,Xue QU,Xiao-xu LI. Few⁃shot image classification method based on sliding feature vectors [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1785-1791.
[13] Li-sheng JIN,Bai-cang GUO,Fang-rong WANG,Jian SHI. Dynamic multiple object detection algorithm for vehicle forward based on improved YOLOv3 [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1427-1436.
[14] Chun-bo WANG,Xiao-qiang DI. Cloud storage integrity verification audit scheme based on label classification [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1364-1369.
[15] Feng-chong LAN,Ji-wen LI,Ji-qing CHEN. DG-SLAM algorithm for dynamic scene compound deep learning and parallel computing [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1437-1446.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd