[1]喻露,戴甜杰,余丽华.基于改进YOLOv5的道路病害智能检测[J].福建工程学院学报,2023,21(04):332-337.[doi:10.3969/j.issn.1672-4348.2023.04.005]
　YU Lu,DAI Tianjie,YU Lihua.Automatic detection of pavement defect based on improved YOLOv5 algorithm[J].Journal of FuJian University of Technology,2023,21(04):332-337.[doi:10.3969/j.issn.1672-4348.2023.04.005]
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基于改进YOLOv5的道路病害智能检测()
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《福建工程学院学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第21卷
期数:
2023年04期
页码:
332-337
栏目:
出版日期:
2023-08-25

文章信息/Info

Title:
Automatic detection of pavement defect based on improved YOLOv5 algorithm
作者:
喻露; 戴甜杰; 余丽华
福建开放大学
Author(s):
YU Lu1;  DAI Tianjie2;  YU Lihua
School of Science and Technology, Fujian Open University
关键词:
目标检测; 改进YOLOv5; 道路裂缝; 自动识别
Keywords:
target detection;  improved YOLOv5;  pavement cracks;  automatic recognition
分类号:
TU997
DOI:
10.3969/j.issn.1672-4348.2023.04.005
文献标志码:
A
摘要:
针对现有道路表观病害检测识别精度低、漏判、误检率高的问题,提出了一种改进的道路表观病害检测高精度识别模型(improved pavement detection?YOLOv5, IPD?YOLOv5)。在YOLOv5的主干特征提取网络中添加由不同空洞卷积组成的ASPP模块,引入SE?Net注意力机制以加强算法从裂缝图像中提取不同尺度特征的能力,实现多尺度特征图的有效融合。结果表明:较传统检测算法,所提的IPD?YOLOv5模型在道路裂缝病害检测上的识别精度最高,其中平均精度比未改进的YOLOv5算法提高了7.47%,漏判率降低了10.29%。
Abstract:
The current pavement defect detection methods suffer from low recognition accuracy, high missingdetection rate, and high falsedetection rate. Thus, an improved highprecision recognition model for pavement defect detection(improved pavement detectionYOLOv5, IPDYOLOv5） was proposed. An ASPP module consisting of various void convolutions was added to the backbone feature extraction network of YOLOv5 algorithm. In addition, the SENet attention mechanism was introduced to enhance the ability of algorithm to extract different scale features from crack images and achieve effective fusion of multiscale feature maps. Results show that the proposed algorithm has the highest detection accuracy for pavement crack defect detection, with an average accuracy improvement of 7.47% and a missingdetection rate reduction of 10.29% compared to the unimproved YOLOv5 algorithm.

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更新日期/Last Update: 2023-08-25