Road Damage Detection Based on Unsupervised Disparity Map Segmentation

Fan, Rui; Liu, Ming

doi:10.1109/tits.2019.2947206

Cited by 95 publications

(45 citation statements)

References 27 publications

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“…However, they did not attempt localization of potholes using thermal images. Fan and Liu [7] proposed a road damage detection using disparity map segmentation using Otsu thresholding. The authors obtained pixel-level road damage detection accuracy of approximately 97.56%.…”

Section: Related Workmentioning

confidence: 99%

Detection and localization of potholes in thermal images using deep neural networks

Gupta

Sharma

et al. 2020

Multimed Tools Appl

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A pothole is a depression caused on roads due to seepage of water into soil structure or weight of continuously moving traffic. This not only damages the suspension of the vehicles but is also a prime reason for road accidents worldwide. This necessitates the need to develop an efficient automatic pothole detection system which can assist concerned authorities for timely repair and maintenance of the roads. This paper proposes a novel approach of bounding box based pothole localization from thermal images using deep neural networks. The modified ResNet34-single shot multibox detector gives an average precision of 74.53% whereas modified ResNet50-RetinaNet model provides 91.15% precision. The results obtained by the proposed modified ResNet50-RetinaNet model are the state-of-the-art results for localization of potholes using thermal images. In real-world scenarios such a system can assist relevant authorities to judge the severity of road damage and take appropriate effective measures accordingly.

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Section: Related Workmentioning

confidence: 99%

Detection and localization of potholes in thermal images using deep neural networks

Gupta

Sharma

et al. 2020

Multimed Tools Appl

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“…The gray histogram is made with the concentration of gray level as abscissa and the corresponding frequency as ordinate. An obvious double peak exists in the gray histogram of the landmark; therefore, Otsu's thresholding method can be applied to find a local threshold [14], [15]. If the difference between the local threshold and the global threshold of the current frame is within a certain range, the local threshold is used for the binarization of the next frame.…”

Section: B Dynamic Thresholdingmentioning

confidence: 99%

Autonomous UAV Landing System Based on Visual Navigation

Fan²,

Han

et al. 2019

2019 IEEE International Conference on Imaging Systems and Techniques (IST)

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In this paper, we present an autonomous unmanned aerial vehicle (UAV) landing system based on visual navigation. We design the landmark as a topological pattern in order to enable the UAV to distinguish the landmark from the environment easily. In addition, a dynamic thresholding method is developed for image binarization to improve detection efficiency. The relative distance in the horizontal plane is calculated according to effective image information, and the relative height is obtained using a linear interpolation method. The landing experiments are performed on a static and a moving platform, respectively. The experimental results illustrate that our proposed landing system performs robustly and accurately.

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“…Road crack detection is an arduous task in the maintenance of traffic infrastructure such as roads, bridges, and airports [1][2][3]. This is because cracks are serious defects in the infrastructure, which will reduce the service life of the facilities and endanger the safety of transportation [4,5].…”

Section: Introductionmentioning

confidence: 99%

RAO‐UNet: a residual attention and octave UNet for road crack detection via balance loss

Fan

Zhao

Liu

et al. 2021

IET Intelligent Trans Sys

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The acquisition and evaluation of road cracks are essential to ensure the availability of roads and necessary maintenance. However, the road cracks images have been obsessed with the problem of imbalance in the category and the number of categories. Among them, the category imbalance makes the network focus on the background and the detection result will be complete black. The imbalanced number of categories leads to the missed detection of thin cracks. In addition, a large number of images generated in real time put forward higher requirements on memory and calculations. The RAO‐UNet is built which is an efficient and effective network for crack detection in road images using encoder–decoder and residual attention module‐based image frequency relationship. Compared with otheTr methods, RAO‐UNet could learn multiple‐spatial‐frequency features, thus can enhance the differentiation of high‐frequency features while saving the computational cost. Regarding the space optimisation, a novel balance loss function is proposed, which not only solves the balance problem, but also ensures the stability and consistency in the optimisation process. We evaluated RAO‐UNet on public data sets. Compared with state‐of‐the‐art methods, it achieves better performance on processing speed and detection accuracy. Specifically, RAO‐UNet achieves 98.32% / 97.86% Precision, 97.84% / 95.89% Recall, 97.61% / 97.04% F1 score on CFD and AigleRN data sets, respectively.

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Road Damage Detection Based on Unsupervised Disparity Map Segmentation

Cited by 95 publications

References 27 publications

Detection and localization of potholes in thermal images using deep neural networks

Detection and localization of potholes in thermal images using deep neural networks

Autonomous UAV Landing System Based on Visual Navigation

RAO‐UNet: a residual attention and octave UNet for road crack detection via balance loss

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