An image-based convolutional neural network system for road defects detection

Benallal M.A.; Tayeb M.S.

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An image-based convolutional neural network system for road defects detection

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An application of convolutional neural network (CNN) technique for road surface defects detection is presented in this paper. You only look ones (YOLO) algorithm showed its capabilities as an effective object detection technique in many previous works for different problems. Road damages detection and classification is one of the most challenging problems faced by public and private road management agencies. We present here results for a first attempt on apply-ing YOLO to detect cracks and potholes, the most common defects encountered in surface roadways. Image database of the Brazilian highways were used to prepare input data, train the model and test it. Despite considering different types of cracks in one class and a less amount of potholes images, results show that the YOLO algorithm performs well with a global rate of 91 of defect de-tection. Output results analysis induce us to work on providing a local database for Algerian roadways with a large number of defect images/videos, as well as producing an automatic road-dedicated defects detector device. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

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《IAES International Journal of Artificial Intelligence》 |2023年第2期|577-584|共8页
作者
Benallal M.A.; Tayeb M.S.;
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Laboratoire SIMPA (Signal-Image-Parole), Département d’informatique, Faculté des Mathématiques et Informatique, Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf (USTO-MB);

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正文语种英语
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Convolutional neural network; Defects detection; Image recognition; Road damages; You only look ones;

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An image-based convolutional neural network system for road defects detection

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