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

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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

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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
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