Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (3): 744-751.DOI: 10.11772/j.issn.1001-9081.2022020252
• Artificial intelligence • Previous Articles
Jie SUN1, Shaoxin WU1, Xuejun WANG2, Jing HUA1()
Received:
2022-03-04
Revised:
2022-05-27
Accepted:
2022-05-30
Online:
2022-08-16
Published:
2023-03-10
Contact:
Jing HUA
About author:
SUN Jie, born in 1985, M. S., intermediate experimentalist. His research interests include image and video processing, visual analysis.Supported by:
通讯作者:
华璟
作者简介:
孙杰(1985—),男,浙江杭州人,中级实验师,硕士,CCF会员,主要研究方向:图像与视频处理、可视化分析基金资助:
CLC Number:
Jie SUN, Shaoxin WU, Xuejun WANG, Jing HUA. Efficient person search algorithm and optimization with Sophon SC5+ chip architecture[J]. Journal of Computer Applications, 2023, 43(3): 744-751.
孙杰, 吴绍鑫, 王学军, 华璟. 基于Sophon SC5+芯片构架的行人搜索算法与优化[J]. 《计算机应用》唯一官方网站, 2023, 43(3): 744-751.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022020252
算法 | 浮点运算量/ GFLOPs | mAP/% | 推理 时间/ms | 模型 大小/MB |
---|---|---|---|---|
YOLOv5s | 15.8 | 82.1 | 5.3 | 14.4 |
YOLOv5s-Ghost | 7.9 | 80.2 | 4.8 | 10.5 |
YOLOv5s-CBAM | 16.0 | 82.9 | 5.4 | 14.6 |
YOLOv5s-GC | 8.0 | 81.3 | 4.9 | 10.7 |
YOLOv5l | 107.9 | 84.3 | 15.9 | 92.8 |
YOLOv5l-Ghost | 42.3 | 82.4 | 12.4 | 49.1 |
Tab. 1 Influence of each module on person detection algorithm
算法 | 浮点运算量/ GFLOPs | mAP/% | 推理 时间/ms | 模型 大小/MB |
---|---|---|---|---|
YOLOv5s | 15.8 | 82.1 | 5.3 | 14.4 |
YOLOv5s-Ghost | 7.9 | 80.2 | 4.8 | 10.5 |
YOLOv5s-CBAM | 16.0 | 82.9 | 5.4 | 14.6 |
YOLOv5s-GC | 8.0 | 81.3 | 4.9 | 10.7 |
YOLOv5l | 107.9 | 84.3 | 15.9 | 92.8 |
YOLOv5l-Ghost | 42.3 | 82.4 | 12.4 | 49.1 |
算法 | 模型大小/MB | mAP/% | 帧率/(frame·s-1) |
---|---|---|---|
YOLOv3[ | 246.3 | 62.8 | 58 |
YOLOv4[ | 256.0 | 70.3 | 50 |
YOLOv4-CSP[ | 210.2 | 67.4 | 53 |
YOLOv3-tiny[ | 34.7 | 37.5 | 267 |
YOLOv4-tiny[ | 23.5 | 45.6 | 301 |
YOLOv5s | 14.4 | 82.1 | 189 |
YOLOv5s-GC | 10.7 | 81.3 | 204 |
Tab. 2 Comparison of experimental results between YOLOv5s-GC and other YOLO algorithms
算法 | 模型大小/MB | mAP/% | 帧率/(frame·s-1) |
---|---|---|---|
YOLOv3[ | 246.3 | 62.8 | 58 |
YOLOv4[ | 256.0 | 70.3 | 50 |
YOLOv4-CSP[ | 210.2 | 67.4 | 53 |
YOLOv3-tiny[ | 34.7 | 37.5 | 267 |
YOLOv4-tiny[ | 23.5 | 45.6 | 301 |
YOLOv5s | 14.4 | 82.1 | 189 |
YOLOv5s-GC | 10.7 | 81.3 | 204 |
算法 | Market-1501 | DukeMTMC-ReID | ||
---|---|---|---|---|
mAP | Rank-1 | mAP | Rank-1 | |
Baseline | 85.9 | 94.5 | 76.4 | 86.4 |
Baseline+ | 86.3 | 94.6 | 76.8 | 86.7 |
Baseline+ | 86.1 | 94.5 | 76.7 | 86.6 |
Baseline+ | 86.5 | 94.7 | 77.0 | 87.0 |
Baseline+Non-local | 87.0 | 94.9 | 77.1 | 87.2 |
ReID | 87.4 | 95.1 | 77.6 | 87.9 |
Tab. 3 Results of ablation experiment
算法 | Market-1501 | DukeMTMC-ReID | ||
---|---|---|---|---|
mAP | Rank-1 | mAP | Rank-1 | |
Baseline | 85.9 | 94.5 | 76.4 | 86.4 |
Baseline+ | 86.3 | 94.6 | 76.8 | 86.7 |
Baseline+ | 86.1 | 94.5 | 76.7 | 86.6 |
Baseline+ | 86.5 | 94.7 | 77.0 | 87.0 |
Baseline+Non-local | 87.0 | 94.9 | 77.1 | 87.2 |
ReID | 87.4 | 95.1 | 77.6 | 87.9 |
检测算法 | Market-1501 | DukeMTMC-ReID | ||
---|---|---|---|---|
mAP | Rank-1 | mAP | Rank-1 | |
ACRN | 62.6 | 83.6 | 51.9 | 72.6 |
SVDNet | 62.1 | 82.3 | 56.8 | 76.7 |
MMT-500 | 71.2 | 87.7 | 53.4 | 73.0 |
GPR | 71.5 | 88.1 | 65.2 | 79.5 |
ConsAtt | 84.7 | 96.1 | 73.1 | 86.3 |
PCB-RPP | 81.6 | 93.8 | 69.2 | 83.3 |
PPS | 85.3 | 94.3 | 75.9 | 88.2 |
BagTricks | 85.9 | 94.5 | 76.4 | 86.4 |
本文算法 | 87.4 | 95.1 | 77.6 | 87.9 |
Tab. 4 Comparison of experimental results of different person re-identification algorithms
检测算法 | Market-1501 | DukeMTMC-ReID | ||
---|---|---|---|---|
mAP | Rank-1 | mAP | Rank-1 | |
ACRN | 62.6 | 83.6 | 51.9 | 72.6 |
SVDNet | 62.1 | 82.3 | 56.8 | 76.7 |
MMT-500 | 71.2 | 87.7 | 53.4 | 73.0 |
GPR | 71.5 | 88.1 | 65.2 | 79.5 |
ConsAtt | 84.7 | 96.1 | 73.1 | 86.3 |
PCB-RPP | 81.6 | 93.8 | 69.2 | 83.3 |
PPS | 85.3 | 94.3 | 75.9 | 88.2 |
BagTricks | 85.9 | 94.5 | 76.4 | 86.4 |
本文算法 | 87.4 | 95.1 | 77.6 | 87.9 |
Market-1501 | DukeMTMC-ReID | ||||
---|---|---|---|---|---|
mAP | Rank-1 | mAP | Rank-1 | ||
10 | 30 | 86.1 | 94.6 | 76.3 | 87.1 |
30 | 50 | 87.1 | 94.9 | 77.0 | 87.4 |
30 | 70 | 87.4 | 95.1 | 77.6 | 87.9 |
50 | 70 | 87.2 | 95.2 | 77.2 | 87.8 |
30 | 100 | 85.9 | 94.3 | 75.8 | 86.8 |
50 | 100 | 86.0 | 94.7 | 76.1 | 87.0 |
70 | 100 | 86.4 | 94.6 | 76.7 | 87.3 |
Tab. 5 Performance comparison of different parameters
Market-1501 | DukeMTMC-ReID | ||||
---|---|---|---|---|---|
mAP | Rank-1 | mAP | Rank-1 | ||
10 | 30 | 86.1 | 94.6 | 76.3 | 87.1 |
30 | 50 | 87.1 | 94.9 | 77.0 | 87.4 |
30 | 70 | 87.4 | 95.1 | 77.6 | 87.9 |
50 | 70 | 87.2 | 95.2 | 77.2 | 87.8 |
30 | 100 | 85.9 | 94.3 | 75.8 | 86.8 |
50 | 100 | 86.0 | 94.7 | 76.1 | 87.0 |
70 | 100 | 86.4 | 94.6 | 76.7 | 87.3 |
模块 | 算法 | mAP/% | 模型大小/MB | 推理时间/ms | |||||
---|---|---|---|---|---|---|---|---|---|
fp32 | int8 | 2080Ti | fp32 | int8 | 2080Ti | fp32 | int8 | ||
行人检测 | YOLOv5s | 82.1 | 80.5 | 28.3 | 28.8 | 7.7 | 5.3 | 4.5 | 3.6 |
YOLOv5s-GC | 81.3 | 79.3 | 22.2 | 23.0 | 6.2 | 4.9 | 4.1 | 3.2 | |
行人重识别 | MMT | 71.2 | 70.4 | 94.4 | 94.9 | 24.3 | 9.4 | 8.1 | 4.6 |
MEB | 72.7 | 71.0 | 28.9 | 29.8 | 8.7 | 14.3 | 6.9 | 4.1 | |
Fast-ReID | 88.6 | 86.9 | 102.6 | 103.1 | 56.9 | 13.7 | 11.2 | 10.7 | |
MLCReID | 45.5 | 43.1 | 102.7 | 103.6 | 24.3 | 13.3 | 10.6 | 5.9 | |
ReID | 87.4 | 85.7 | 100.7 | 94.9 | 24.3 | 8.6 | 7.3 | 4.1 |
Tab. 6 Comparison of results after quantization by different algorithms
模块 | 算法 | mAP/% | 模型大小/MB | 推理时间/ms | |||||
---|---|---|---|---|---|---|---|---|---|
fp32 | int8 | 2080Ti | fp32 | int8 | 2080Ti | fp32 | int8 | ||
行人检测 | YOLOv5s | 82.1 | 80.5 | 28.3 | 28.8 | 7.7 | 5.3 | 4.5 | 3.6 |
YOLOv5s-GC | 81.3 | 79.3 | 22.2 | 23.0 | 6.2 | 4.9 | 4.1 | 3.2 | |
行人重识别 | MMT | 71.2 | 70.4 | 94.4 | 94.9 | 24.3 | 9.4 | 8.1 | 4.6 |
MEB | 72.7 | 71.0 | 28.9 | 29.8 | 8.7 | 14.3 | 6.9 | 4.1 | |
Fast-ReID | 88.6 | 86.9 | 102.6 | 103.1 | 56.9 | 13.7 | 11.2 | 10.7 | |
MLCReID | 45.5 | 43.1 | 102.7 | 103.6 | 24.3 | 13.3 | 10.6 | 5.9 | |
ReID | 87.4 | 85.7 | 100.7 | 94.9 | 24.3 | 8.6 | 7.3 | 4.1 |
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