Optics and Precision Engineering, Volume. 31, Issue 10, 1532(2023)
Dense pedestrian detection algorithm in multi-branch non-anchor frame network
Figures & Tables(17)
Table 1. Experimental training environment configuration
View tableView in ArticleTable 1. Experimental training environment configuration
名称 配置 CPU Intel Xeon E5-2678 v3 显卡 NVIDIA Tesla K80 加速器 CUDA，CUDNN
Table 2. Result of system scale calibration
View tableView in ArticleTable 2. Result of system scale calibration
Methods mAP/% F1 Prec/% Recall/% FPS Params Baseline 32.18 0.47 41.70 53.31 14.39 32.78 Baseline+hourglass+1FEN（global） 61.45 0.67 68.93 65.50 13.38 32.78 Baseline+hourglass+2FEN（global+head） 68.32 0.78 75.31 79.86 12.42 32.78 Baseline+hourglass+3FEN（global+head+legs） 75.04 0.83 78.89 86.55 11.19 32.78 Baseline+hourglass+3FEN+LFD 85.22 0.87 80.07 94.39 11.10 32.79
Table 3. Visualization of each branch output of MBAN
Table 4. Fig.4 Output visual of different network structure models
View tableView in ArticleTable 4. Fig.4 Output visual of different network structure models
原图 Baseline Baseline+2FEN Baseline+3FEN+LFD（MBAN）
Table 5. Comparison results between MBAN method and other methods
View tableView in ArticleTable 5. Comparison results between MBAN method and other methods
Methods Backbone Size mAP/% Params（×106） GFLOPs FPS VGG16-SSD VGG16 512×512 69.16 60.3 22.9 35 YOLOv3 Darknet53 640×640 84.22 61.5 154.9 27 YOLOv3-tiny Darknet19 640×640 68.23 8.0 12.9 100 ResNet50-FCOS Resnet50 640×640 69.66 50.2 195.9 22 CenterNet Hourglass104 512×512 63.16 25.7 22.1 14. MBAN（CrowHuman） Resnet50 512×512 86.98 32.8 23.8 11 MBAN（WiderPerson） Resnet50 512×512 85.22 32.8 23.8 11
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Zhixuan LÜ, Xia WEI, Deqi HUANG. Dense pedestrian detection algorithm in multi-branch non-anchor frame network[J]. Optics and Precision Engineering, 2023, 31(10): 1532
Paper Information
Category: Information Sciences
Received: May. 30, 2022
Accepted: --
Published Online: Jul. 4, 2023
The Author Email: WEI Xia (30462111@qq.com)
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