Abstract
Cracks on road surfaces undermine infrastructure load-bearing capacity and endanger both motorists and pedestrians. Prompt and effective identification of road cracks is vital to swiftly address repairs and prevent their escalation and further structural decay. Presently, the majority of crack detection approaches rely on labor-intensive manual inspection rather than automated image-based methods, resulting in costly and time-consuming processes. Automated crack detection methods are needed to streamline the process, reduce costs, and enable more proactive maintenance efforts to ensure road safety and longevity. This paper presents a comprehensive study on road crack detection, aiming to develop an accurate and efficient system for identifying cracks on road surfaces. Leveraging deep learning techniques, the proposed approach utilizes a two-stage convolutional neural network (CNN) combined with the extreme learning machine (ELM) algorithm. Through extensive experimentation and evaluation, the model demonstrates superior performance in detecting road cracks, contributing to proactive maintenance strategies, and enhancing road safety. An accuracy of 84.98% and an F-measure of 84.57% highlight the potential of the proposed approach in automating road crack detection compared to the existing deep learning approaches.
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Roul, R.K., Navpreet, Sahoo, J.K. (2024). Intelligent Ensemble-Based Road Crack Detection: A Holistic View. In: Devismes, S., Mandal, P.S., Saradhi, V.V., Prasad, B., Molla, A.R., Sharma, G. (eds) Distributed Computing and Intelligent Technology. ICDCIT 2024. Lecture Notes in Computer Science, vol 14501. Springer, Cham. https://doi.org/10.1007/978-3-031-50583-6_21
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