Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (8): 2378-2385.DOI: 10.11772/j.issn.1001-9081.2021061005
• Artificial intelligence • Previous Articles
Xinyu ZHANG1,2, Sheng DING1,2(), Zhipei YANG1,2
Received:
2021-06-15
Revised:
2021-10-03
Accepted:
2021-10-18
Online:
2022-01-25
Published:
2022-08-10
Contact:
Sheng DING
About author:
ZHANG Xinyu, born in 1996, M. S. candidate. His research interests include computer vision, deep learning.Supported by:
通讯作者:
丁胜
作者简介:
张新宇(1996—),男,河南焦作人,硕士研究生,主要研究方向:计算机视觉、深度学习;基金资助:
CLC Number:
Xinyu ZHANG, Sheng DING, Zhipei YANG. Traffic sign detection algorithm based on improved attention mechanism[J]. Journal of Computer Applications, 2022, 42(8): 2378-2385.
张新宇, 丁胜, 杨治佩. 基于改进注意力机制的交通标志检测算法[J]. 《计算机应用》唯一官方网站, 2022, 42(8): 2378-2385.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021061005
特征图 | 感受野 | Anchor | ||
---|---|---|---|---|
19×19 | 大 | (24,39) | (35,73) | 72,116 |
38×38 | 中 | (16,26) | (17,47) | (22,59) |
76×76 | 小 | (10,26) | (12,23) | (14,40) |
Tab. 1 Anchors generated by k-means++ algorithm
特征图 | 感受野 | Anchor | ||
---|---|---|---|---|
19×19 | 大 | (24,39) | (35,73) | 72,116 |
38×38 | 中 | (16,26) | (17,47) | (22,59) |
76×76 | 小 | (10,26) | (12,23) | (14,40) |
算法 | 额外参数量 | 额外计算量 | mAP/% |
---|---|---|---|
YOLOv4 | 0 | 0 | 95.47 |
YOLOv4+FcaNet | 1 136 320 | 121 831 468 | 96.19 |
YOLOv4+FcaNet-E | 5 206 | 36 506 | 96.28 |
Tab. 2 Comparison of YOLOv4 algorithms before and after introducing channel attention module
算法 | 额外参数量 | 额外计算量 | mAP/% |
---|---|---|---|
YOLOv4 | 0 | 0 | 95.47 |
YOLOv4+FcaNet | 1 136 320 | 121 831 468 | 96.19 |
YOLOv4+FcaNet-E | 5 206 | 36 506 | 96.28 |
算法 | 额外计算量 | 单张耗时/s | mAP/% |
---|---|---|---|
CBAM | 44 648 488 | 0.041 | 96.55 |
改进后的CBAM | 29 773 | 0.025 | 93.19 |
FcaNet-E | 36 506 | 0.025 | 96.28 |
Tab. 3 Comparison of FcaNet-E and CBAM attention mechanism modules
算法 | 额外计算量 | 单张耗时/s | mAP/% |
---|---|---|---|
CBAM | 44 648 488 | 0.041 | 96.55 |
改进后的CBAM | 29 773 | 0.025 | 93.19 |
FcaNet-E | 36 506 | 0.025 | 96.28 |
算法 | 单张耗时/s | AP/% | mAP/% | ||
---|---|---|---|---|---|
禁止 | 指示 | 警告 | |||
Faster R-CNN | 0.142 | 99.16 | 92.47 | 91.82 | 94.48 |
YOLOv3 | 0.034 | 99.02 | 92.31 | 88.28 | 93.20 |
YOLOv4 | 0.019 | 93.14 | 96.69 | 96.57 | 95.47 |
YOLOv5s | 0.015 | 93.17 | 91.48 | 95.79 | 93.48 |
本文算法 | 0.025 | 96.48 | 96.73 | 97.42 | 96.88 |
Tab. 4 Comparison results of different algorithms on CCTSDB dataset(IoU=0.5)
算法 | 单张耗时/s | AP/% | mAP/% | ||
---|---|---|---|---|---|
禁止 | 指示 | 警告 | |||
Faster R-CNN | 0.142 | 99.16 | 92.47 | 91.82 | 94.48 |
YOLOv3 | 0.034 | 99.02 | 92.31 | 88.28 | 93.20 |
YOLOv4 | 0.019 | 93.14 | 96.69 | 96.57 | 95.47 |
YOLOv5s | 0.015 | 93.17 | 91.48 | 95.79 | 93.48 |
本文算法 | 0.025 | 96.48 | 96.73 | 97.42 | 96.88 |
FcaNet | FcaNet-E | SCRFB | mAP |
---|---|---|---|
√ | 96.19 | ||
√ | 96.28 | ||
√ | 96.09 | ||
√ | √ | 96.71 | |
√ | √ | 96.88 | |
95.47 |
Tab. 5 mAP comparison after adding each module to YOLOv4 network
FcaNet | FcaNet-E | SCRFB | mAP |
---|---|---|---|
√ | 96.19 | ||
√ | 96.28 | ||
√ | 96.09 | ||
√ | √ | 96.71 | |
√ | √ | 96.88 | |
95.47 |
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