A Fast and Accurate Automated Pavement Crack Detection Algorithm. Chatterjee, A. & Tsai, Y. In 2018 26th European Signal Processing Conference (EUSIPCO), pages 2140-2144, Sep., 2018.
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Paper doi abstract bibtex
Paper doi abstract bibtex Over the last 20 years, several crack detection algorithms have been developed to implement safe and efficient automated road condition survey (ARCS) systems. Although the current state-of-the-art algorithms can achieve a high level of accuracy, their computation time makes them infeasible to implement in real-time without massive parallelization. This paper presents a fast and accurate crack detection algorithm. The algorithm consists of the following major steps: 1) Image preprocessing; 2) Preliminary crack segmentation to minimize false negatives; 3) Crack object generation and connection to remove false positives; and 4) Refinement of the crack segmentation through a minimal path search based procedure. The proposed algorithm achieves an overall score of 80 in the Crack Detection Algorithm Performance Evaluation System (CDA-PES). With a median processing time of 0.52 seconds for 0.65 megapixel images on a single CPU thread, this algorithm makes accurate, real-time processing viable. The research presented in this paper contributes towards more widespread adoption of safer and efficient automated road condition surveys.
@InProceedings{8553388,
author = {A. Chatterjee and Y. Tsai},
booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
title = {A Fast and Accurate Automated Pavement Crack Detection Algorithm},
year = {2018},
pages = {2140-2144},
abstract = {Over the last 20 years, several crack detection algorithms have been developed to implement safe and efficient automated road condition survey (ARCS) systems. Although the current state-of-the-art algorithms can achieve a high level of accuracy, their computation time makes them infeasible to implement in real-time without massive parallelization. This paper presents a fast and accurate crack detection algorithm. The algorithm consists of the following major steps: 1) Image preprocessing; 2) Preliminary crack segmentation to minimize false negatives; 3) Crack object generation and connection to remove false positives; and 4) Refinement of the crack segmentation through a minimal path search based procedure. The proposed algorithm achieves an overall score of 80 in the Crack Detection Algorithm Performance Evaluation System (CDA-PES). With a median processing time of 0.52 seconds for 0.65 megapixel images on a single CPU thread, this algorithm makes accurate, real-time processing viable. The research presented in this paper contributes towards more widespread adoption of safer and efficient automated road condition surveys.},
keywords = {crack detection;image classification;image segmentation;roads;search problems;real-time processing;minimal path search;crack object generation;image preprocessing;automated road condition surveys;automated pavement crack detection;safer road condition surveys;median processing time;minimal path search based procedure;crack segmentation;accurate crack detection algorithm;Signal processing algorithms;Image segmentation;Detection algorithms;Roads;Europe;Signal processing;Sensors},
doi = {10.23919/EUSIPCO.2018.8553388},
issn = {2076-1465},
month = {Sep.},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570439781.pdf},
}Downloads: 0
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This paper presents a fast and accurate crack detection algorithm. The algorithm consists of the following major steps: 1) Image preprocessing; 2) Preliminary crack segmentation to minimize false negatives; 3) Crack object generation and connection to remove false positives; and 4) Refinement of the crack segmentation through a minimal path search based procedure. The proposed algorithm achieves an overall score of 80 in the Crack Detection Algorithm Performance Evaluation System (CDA-PES). With a median processing time of 0.52 seconds for 0.65 megapixel images on a single CPU thread, this algorithm makes accurate, real-time processing viable. 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