A triple-thresholds pavement crack detection method leveraging random structured forest

Peng, Cheng; Yang, Mo; Zheng, Qinghe; Zhang, Jiong; Wang, Deqiang; Yan, Ruyu; Wang, Jiaxing; Li, Bangjun

doi:10.1016/j.conbuildmat.2020.120080

Cited by 65 publications

(30 citation statements)

References 40 publications

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“…Zou et al [12] proposed a crack tree noting the effect of lighting on pavement detection and proposed a shadow removal algorithm before crack extraction to eliminate the effect of shadows on the extraction results. However, the crack recognition requires the assistance of some machine learning algorithms such as SVM (Support Vector Machine), RBF (Radial Basis Function), KNN (K-Nearest Neighbor) and Random Decision Forest [10,13,14]. Also, statistical features, gray-level features, texture and shape features of cracked images are increasingly used for feature extraction of the images.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Thermal Image Analysis for Pavement Defect Detection and Classification Considering Complex Pavement Conditions

et al. 2021

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Automatic damage detection using deep learning warrants an extensive data source that captures complex pavement conditions. This paper proposes a thermal-RGB fusion image-based pavement damage detection model, wherein the fused RGB-thermal image is formed through multi-source sensor information to achieve fast and accurate defect detection including complex pavement conditions. The proposed method uses pre-trained EfficientNet B4 as the backbone architecture and generates an argument dataset (containing non-uniform illumination, camera noise, and scales of thermal images too) to achieve high pavement damage detection accuracy. This paper tests separately the performance of different input data (RGB, thermal, MSX, and fused image) to test the influence of input data and network on the detection results. The results proved that the fused image’s damage detection accuracy can be as high as 98.34% and by using the dataset after augmentation, the detection model deems to be more stable to achieve 98.35% precision, 98.34% recall, and 98.34% F1-score.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Thermal Image Analysis for Pavement Defect Detection and Classification Considering Complex Pavement Conditions

et al. 2021

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“…It is suitable for demonstrating the nonlinear effect of variables, and it can model complex interactions among variables (Chen et al 2020;Chencho et al 2020;Kou et al 2020;). However, the common RF has difficulty coping with seasonal and periodic changes in influent water quality (Peng et al 2020;Yang et al 2020). Therefore, the IRF model is developed, which is a hybrid model consisting of long-term parts and short-term parts.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Estimating effluent turbidity in the drinking water flocculation process with an improved random forest model

Wang

Chang

et al. 2021

Water Supply

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During the drinking water treatment, the uncertain changes of raw water quality bring great difficulties to the control of flocculants dosage, especially because the feedback information based on the effluent turbidimeter of sedimentation tank can only be obtained after a long time when the influent water quality changes for the large lag characteristics of flocculation process. The prediction of effluent turbidity of sedimentation tank can effectively solve aforementioned problem. Given that it is difficult for the ordinary random forest (RF) model to accurately predict the effluent turbidity of sedimentation tank for the complicated changes of raw water quality, an improved random forest (IRF) model composed of long-term and short-term parts is proposed, which can capture the periodicity and time-varying characteristics of influent water quality data. The experimental results show that, the root mean square error and mean absolute percentage error of IRF model in Baiyangwan waterworks are improved 67.52% and 67.91% respectively, compared with those of ordinary RF model. The proposed effluent turbidity predictions are also successfully developed in Xujiang waterworks and Xiangcheng waterworks of Suzhou, China. This research provides an effective method for real-time predicting the effluent turbidity of sedimentation tank according to the influent water quality data.

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“…To address the aforementioned task, several methods designed for crack segmentation/detection have been proposed [4,5,6,7,8,9,10,11]. Zhou et al [4] propose Deep-Crack which learns multi-scale deep features to capture line Input Image Initial Pred.…”

Section: Introductionmentioning

confidence: 99%

Crack Detection and Refinement Via Deep Reinforcement Learning

Park

Chen

2021

2021 IEEE International Conference on Image Processing (ICIP)

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Detecting small cracks in concrete is difficult due to the complexity and thinness of cracking patterns, which requires the development of refined vision-based segmentation algorithms that can accurately characterize the details of crack defects. While existing methods are good at generally outlining cracks, due to inherent differences in shape distributions between common objects and cracks, their predictions often have disconnected segments and inaccuracy along boundaries. To this end, we develop a refinement framework using reinforcement learning (RL) that can better recognize details specific to cracks. Our method uses an RL agent to iteratively improve per-pixel crack predictions of a general segmentation model. We find that in addition to connecting gaps in predictions, the RL agent is also able to detect cracks that are missed in the original predictions. It does so by using the originally detected regions as crack priors to branch out from. Refining outputs of a commonly used per-pixel segmentation model, our method outperforms the current state-of-theart approaches for crack segmentation. Our experiments also demonstrate that our method generalizes well to a similar task of vessel segmentation.

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A triple-thresholds pavement crack detection method leveraging random structured forest

Cited by 65 publications

References 40 publications

Deep Learning-Based Thermal Image Analysis for Pavement Defect Detection and Classification Considering Complex Pavement Conditions

Deep Learning-Based Thermal Image Analysis for Pavement Defect Detection and Classification Considering Complex Pavement Conditions

Estimating effluent turbidity in the drinking water flocculation process with an improved random forest model

Crack Detection and Refinement Via Deep Reinforcement Learning

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