This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to pr...This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.展开更多
The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality perc...The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality percentageamong other Asian countries. The main reasons for RTAs are roadcracks and potholes. Understanding the need for an automated system forthe detection of cracks and potholes, this study proposes a decision supportsystem (DSS) for an autonomous road information system for smart citydevelopment with the use of deep learning. The proposed DSS works in layerswhere initially the image of roads is captured and coordinates attached to theimage with the help of global positioning system (GPS), communicated tothe decision layer to find about the cracks and potholes in the roads, andeventually, that information is passed to the road management informationsystem, which gives information to drivers and the maintenance department.For the decision layer, we projected a CNN-based model for pothole crackdetection (PCD). Aimed at training, a K-fold cross-validation strategy wasused where the value of K was set to 10. The training of PCD was completedwith a self-collected dataset consisting of 6000 images from Pakistani roads.The proposed PCD achieved 98% of precision, 97% recall, and accuracy whiletesting on unseen images. The results produced by our model are higher thanthe existing model in terms of performance and computational cost, whichproves its significance.展开更多
针对裂缝自动检测任务中难以获取大量精确标注样本数据的问题,提出LGS-Net(Local Global Similarity-Network)模型。LGS-Net的核心在于利用裂缝图像区域的语义相似性,有效结合少量已标注数据和大量未标注图像数据,通过半监督学习实现裂...针对裂缝自动检测任务中难以获取大量精确标注样本数据的问题,提出LGS-Net(Local Global Similarity-Network)模型。LGS-Net的核心在于利用裂缝图像区域的语义相似性,有效结合少量已标注数据和大量未标注图像数据,通过半监督学习实现裂缝自动检测。为全面评估LGS-Net的性能,实验在GAPs384和Crack500数据集上进行验证。结果表明,在标注资源有限的情况下,LGS-Net能够实现高精度的裂缝检测。通过对检测结果的可视化分析,证明LGS-Net具有在复杂环境下有效识别裂缝的能力。LGS-Net利用路面裂缝图像的语义相似性特征进行检测,能为路面裂缝检测的工程应用提供技术支持。展开更多
文摘This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.
基金Hunan Provincial Science and Technology Innovation Leader Project,Grant/Award Number:2021RC4025National Natural ScienceFoundation of China,Grant/Award Number:51808209Hunan Provincial Innovation Foundation for Postgraduate,Grant/Award Number:QL20210106.
文摘The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality percentageamong other Asian countries. The main reasons for RTAs are roadcracks and potholes. Understanding the need for an automated system forthe detection of cracks and potholes, this study proposes a decision supportsystem (DSS) for an autonomous road information system for smart citydevelopment with the use of deep learning. The proposed DSS works in layerswhere initially the image of roads is captured and coordinates attached to theimage with the help of global positioning system (GPS), communicated tothe decision layer to find about the cracks and potholes in the roads, andeventually, that information is passed to the road management informationsystem, which gives information to drivers and the maintenance department.For the decision layer, we projected a CNN-based model for pothole crackdetection (PCD). Aimed at training, a K-fold cross-validation strategy wasused where the value of K was set to 10. The training of PCD was completedwith a self-collected dataset consisting of 6000 images from Pakistani roads.The proposed PCD achieved 98% of precision, 97% recall, and accuracy whiletesting on unseen images. The results produced by our model are higher thanthe existing model in terms of performance and computational cost, whichproves its significance.
文摘针对裂缝自动检测任务中难以获取大量精确标注样本数据的问题,提出LGS-Net(Local Global Similarity-Network)模型。LGS-Net的核心在于利用裂缝图像区域的语义相似性,有效结合少量已标注数据和大量未标注图像数据,通过半监督学习实现裂缝自动检测。为全面评估LGS-Net的性能,实验在GAPs384和Crack500数据集上进行验证。结果表明,在标注资源有限的情况下,LGS-Net能够实现高精度的裂缝检测。通过对检测结果的可视化分析,证明LGS-Net具有在复杂环境下有效识别裂缝的能力。LGS-Net利用路面裂缝图像的语义相似性特征进行检测,能为路面裂缝检测的工程应用提供技术支持。
