《计算机应用》唯一官方网站 ›› 2023, Vol. 43 ›› Issue (4): 1284-1290.DOI: 10.11772/j.issn.1001-9081.2022030410
• 多媒体计算与计算机仿真 • 上一篇
收稿日期:
2022-04-02
修回日期:
2022-06-23
接受日期:
2022-07-12
发布日期:
2023-01-11
出版日期:
2023-04-10
通讯作者:
杨景玉
作者简介:
郝巨鸣(1973—),男,甘肃陇南人,高级工程师,主要研究方向:智慧交通;基金资助:
Juming HAO1, Jingyu YANG2(), Shumei HAN2, Yangping WANG2
Received:
2022-04-02
Revised:
2022-06-23
Accepted:
2022-07-12
Online:
2023-01-11
Published:
2023-04-10
Contact:
Jingyu YANG
About author:
HAO Juming, born in 1973, senior engineer. His research interests include intelligent transportation.Supported by:
摘要:
针对目前公路路面裂缝种类和尺度多样导致路面病害检测困难的问题,提出一种基于GhostNet的轻量化无人机图像裂缝检测方法检测不同种类路面裂缝。首先,引入轻量级GhostNet中的Ghost模块优化YOLOv4主干特征提取网络,得到轻量化模型YOLOv4-Light,以降低模型复杂度,并提高裂缝检测速度;然后,在模型预测输出端融合高效通道注意力(ECA)机制,从而进一步增强裂缝特征提取能力,提高裂缝检测精度。仿真实验结果表明,所提方法与现有的YOLOv4相比,模型大小降低了82.31%,模型参数量减少了82.56%,并提高了裂缝检测效率,能够满足公路运输过程中出现的不同类型的裂缝检测需求。
中图分类号:
郝巨鸣, 杨景玉, 韩淑梅, 王阳萍. 引入Ghost模块和ECA的YOLOv4公路路面裂缝检测方法[J]. 计算机应用, 2023, 43(4): 1284-1290.
Juming HAO, Jingyu YANG, Shumei HAN, Yangping WANG. YOLOv4 highway pavement crack detection method using Ghost module and ECA[J]. Journal of Computer Applications, 2023, 43(4): 1284-1290.
特征图 | 操作 | 输入 | 输出 | 核 | SE | Stride |
---|---|---|---|---|---|---|
76×76×27 | Conv2D | (608,608,3) | (304,304,16) | (3,3) | 2 | |
GhostBN1 | (304,304,16) | (304,304,16) | (3,3) | × | 1 | |
GhostBN2 | (304,304,16) | (304,304,16) | (3,3) | × | 1 | |
(304,304,16) | (152,152,24) | (3,3) | × | 2 | ||
GhostBN2 | (152,152,24) | (76,76,40) | (5,5) | √ | 2 | |
(76,76,40) | (76,76,40) | (5,5) | √ | 1 | ||
38×38×27 | GhostBN4 | (76,76,40) | (38,38,80) | (3,3) | × | 1 |
(38,38,80) | (38,38,80) | (3,3) | × | 1 | ||
(38,38,80) | (38,38,80) | (3,3) | × | 1 | ||
(38,38,80) | (38,38,80) | (3,3) | × | 1 | ||
GhostBN2 | (38,38,80) | (38,38,112) | (3,3) | √ | 1 | |
(38,38,112) | (38,38,112) | (3,3) | √ | 1 | ||
19×19×27 | GhostBN5 | (38,38,112) | (19,19,160) | (5,5) | √ | 2 |
(19,19,160) | (19,19,160) | (5,5) | × | 1 | ||
(19,19,160) | (19,19,160) | (5,5) | √ | 1 | ||
(19,19,160) | (19,19,160) | (5,5) | × | 1 | ||
(19,19,160) | (19,19,160) | (5,5) | √ | 1 |
表1 GhostNet参数
Tab. 1 GhostNet parameters
特征图 | 操作 | 输入 | 输出 | 核 | SE | Stride |
---|---|---|---|---|---|---|
76×76×27 | Conv2D | (608,608,3) | (304,304,16) | (3,3) | 2 | |
GhostBN1 | (304,304,16) | (304,304,16) | (3,3) | × | 1 | |
GhostBN2 | (304,304,16) | (304,304,16) | (3,3) | × | 1 | |
(304,304,16) | (152,152,24) | (3,3) | × | 2 | ||
GhostBN2 | (152,152,24) | (76,76,40) | (5,5) | √ | 2 | |
(76,76,40) | (76,76,40) | (5,5) | √ | 1 | ||
38×38×27 | GhostBN4 | (76,76,40) | (38,38,80) | (3,3) | × | 1 |
(38,38,80) | (38,38,80) | (3,3) | × | 1 | ||
(38,38,80) | (38,38,80) | (3,3) | × | 1 | ||
(38,38,80) | (38,38,80) | (3,3) | × | 1 | ||
GhostBN2 | (38,38,80) | (38,38,112) | (3,3) | √ | 1 | |
(38,38,112) | (38,38,112) | (3,3) | √ | 1 | ||
19×19×27 | GhostBN5 | (38,38,112) | (19,19,160) | (5,5) | √ | 2 |
(19,19,160) | (19,19,160) | (5,5) | × | 1 | ||
(19,19,160) | (19,19,160) | (5,5) | √ | 1 | ||
(19,19,160) | (19,19,160) | (5,5) | × | 1 | ||
(19,19,160) | (19,19,160) | (5,5) | √ | 1 |
实验 | GhostNet | ECA | mAP/% | 模型大小/MB | 参数量/106 |
---|---|---|---|---|---|
1 | 58.17 | 250.93 | 64.12 | ||
2 | √ | 48.39 | 44.38 | 11.09 | |
3 | √ | 60.35 | 250.50 | 64.19 | |
4 | √ | √ | 56.34 | 44.40 | 11.18 |
表2 消融实验结果对比
Tab. 2 Comparison of results of ablation experiments
实验 | GhostNet | ECA | mAP/% | 模型大小/MB | 参数量/106 |
---|---|---|---|---|---|
1 | 58.17 | 250.93 | 64.12 | ||
2 | √ | 48.39 | 44.38 | 11.09 | |
3 | √ | 60.35 | 250.50 | 64.19 | |
4 | √ | √ | 56.34 | 44.40 | 11.18 |
模型 | AP/% | mAP/% | 模型大小/MB | 参数量/106 | |||
---|---|---|---|---|---|---|---|
LC | TC | MC | RC | ||||
YOLOv3 | 56.33 | 54.61 | 38.45 | 54.74 | 54.74 | 241.26 | 61.60 |
改进YOLOv3[ | 58.19 | 56.27 | 40.16 | 55.97 | 52.65 | 247.39 | 63.54 |
YOLOv4-tiny | 46.26 | 43.18 | 35.24 | 45.69 | 42.59 | 23.37 | 58.92 |
改进的YOLOv4-tiny[ | 50.21 | 44.34 | 38.29 | 48.64 | 45.37 | 31.25 | 61.17 |
YOLOv4 | 60.21 | 56.37 | 43.26 | 58.17 | 58.17 | 250.93 | 64.12 |
本文模型 | 58.42 | 56.14 | 46.19 | 56.34 | 56.34 | 44.40 | 11.18 |
表3 不同模型的检测指标对比结果
Tab. 3 Comparison results of detection indicators of different models
模型 | AP/% | mAP/% | 模型大小/MB | 参数量/106 | |||
---|---|---|---|---|---|---|---|
LC | TC | MC | RC | ||||
YOLOv3 | 56.33 | 54.61 | 38.45 | 54.74 | 54.74 | 241.26 | 61.60 |
改进YOLOv3[ | 58.19 | 56.27 | 40.16 | 55.97 | 52.65 | 247.39 | 63.54 |
YOLOv4-tiny | 46.26 | 43.18 | 35.24 | 45.69 | 42.59 | 23.37 | 58.92 |
改进的YOLOv4-tiny[ | 50.21 | 44.34 | 38.29 | 48.64 | 45.37 | 31.25 | 61.17 |
YOLOv4 | 60.21 | 56.37 | 43.26 | 58.17 | 58.17 | 250.93 | 64.12 |
本文模型 | 58.42 | 56.14 | 46.19 | 56.34 | 56.34 | 44.40 | 11.18 |
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