基于概率叠加边缘检测网络的公路病害识别算法
首发时间:2020-01-14
摘要:公路病害检测在路面养护工作中扮演着重要角色,是促进交通基础设施建设健康平稳推进的基础。而传统的人工检测方法已经难以满足当前庞大公路网对于检测实时性的要求,因此,研究设计一种高效、准确、鲁棒性强的公路病害检测方法对于路面养护工作具有重大的意义。本文重点关注基于数字图像自动化检测路面病害的方法,提出了一种基于概率叠加边缘检测网络的公路病害识别算法,在原概率图基础上增加由复杂病害特征产生的概率增益。实验结果表明,本算法识别准确率明显高于传统方法,尤其对于特殊病害识别效果提升明显。
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Road Disease Detection Algorithm Based on Probability Overlay Edge Detection Network
Abstract:The detection of road diseases plays an important role in the maintenance of roadways and forms the basis for promoting healthy and regular progress in the construction of transport infrastructure. However, the traditional method of manual detection has been difficult to meet the current real-time detection requirements of the huge road network. Therefore, research and design of an efficient, precise and robust method for detecting road diseases is of great importance for the maintenance of pavements. This article focuses on the automatic detection of roadway diseases on the basis of digital images, and proposes a road disease recognition algorithm based on a probabilistic network for detection of overlapping edges, which increases the probability gain generated. by the complex features of the disease based on the original probability map. Experimental results show that the recognition accuracy of this algorithm is much higher than that of traditional methods, especially for the recognition of particular diseases.
Keywords: crack detection convolutional neural network two-channel
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