Speed Bump and Pothole Detection Using Deep Neural Network with Images Captured through ZED Camera
The condition of the roads where cars circulate is of the utmost importance to ensure that each autonomous or manual car can complete its journey satisfactorily. The existence of potholes, speed bumps, and other irregularities in the pavement can cause car wear and fatal traffic accidents. Therefore...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/14/8349 |
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| author | José-Eleazar Peralta-López Joel-Artemio Morales-Viscaya David Lázaro-Mata Marcos-Jesús Villaseñor-Aguilar Juan Prado-Olivarez Francisco-Javier Pérez-Pinal José-Alfredo Padilla-Medina Juan-José Martínez-Nolasco Alejandro-Israel Barranco-Gutiérrez |
| author_facet | José-Eleazar Peralta-López Joel-Artemio Morales-Viscaya David Lázaro-Mata Marcos-Jesús Villaseñor-Aguilar Juan Prado-Olivarez Francisco-Javier Pérez-Pinal José-Alfredo Padilla-Medina Juan-José Martínez-Nolasco Alejandro-Israel Barranco-Gutiérrez |
| author_sort | José-Eleazar Peralta-López |
| collection | DOAJ |
| description | The condition of the roads where cars circulate is of the utmost importance to ensure that each autonomous or manual car can complete its journey satisfactorily. The existence of potholes, speed bumps, and other irregularities in the pavement can cause car wear and fatal traffic accidents. Therefore, detecting and characterizing these anomalies helps reduce the risk of accidents and damage to the vehicle. However, street images are naturally multivariate, with redundant and substantial information, as well as significantly contaminated measurement noise, making the detection of street anomalies more challenging. In this work, an automatic color image analysis using a deep neural network for the detection of potholes on the road using images taken by a ZED camera is proposed. A lightweight architecture was designed to speed up training and usage. This consists of seven properly connected and synchronized layers. All the pixels of the original image are used without resizing. The classic stride and pooling operations were used to obtain as much information as possible. A database was built using a ZED camera seated on the front of a car. The routes where the photographs were taken are located in the city of Celaya in Guanajuato, Mexico. Seven hundred and fourteen images were manually tagged, several of which contain bumps and potholes. The system was trained with 70% of the database and validated with the remaining 30%. In addition, we propose a database that discriminates between potholes and speed bumps. A precision of 98.13% using 37 convolution filters in a 3 × 3 window was obtained, which improves upon recent state-of-the-art work. |
| first_indexed | 2024-03-11T01:19:12Z |
| format | Article |
| id | doaj.art-080a402c49904a319f8da660478a518a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:19:12Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-080a402c49904a319f8da660478a518a2023-11-18T18:11:51ZengMDPI AGApplied Sciences2076-34172023-07-011314834910.3390/app13148349Speed Bump and Pothole Detection Using Deep Neural Network with Images Captured through ZED CameraJosé-Eleazar Peralta-López0Joel-Artemio Morales-Viscaya1David Lázaro-Mata2Marcos-Jesús Villaseñor-Aguilar3Juan Prado-Olivarez4Francisco-Javier Pérez-Pinal5José-Alfredo Padilla-Medina6Juan-José Martínez-Nolasco7Alejandro-Israel Barranco-Gutiérrez8Tecnológico Nacional de México en Celaya (TecNM), Antonio García Cubas, Esquina, Av. Tecnológico, Celaya 38010, MexicoTecnológico Nacional de México en Celaya (TecNM), Antonio García Cubas, Esquina, Av. Tecnológico, Celaya 38010, MexicoTecnológico Nacional de México en Celaya (TecNM), Antonio García Cubas, Esquina, Av. Tecnológico, Celaya 38010, MexicoTecnológico Nacional de México en Celaya (TecNM), Antonio García Cubas, Esquina, Av. Tecnológico, Celaya 38010, MexicoTecnológico Nacional de México en Celaya (TecNM), Antonio García Cubas, Esquina, Av. Tecnológico, Celaya 38010, MexicoTecnológico Nacional de México en Celaya (TecNM), Antonio García Cubas, Esquina, Av. Tecnológico, Celaya 38010, MexicoTecnológico Nacional de México en Celaya (TecNM), Antonio García Cubas, Esquina, Av. Tecnológico, Celaya 38010, MexicoTecnológico Nacional de México en Celaya (TecNM), Antonio García Cubas, Esquina, Av. Tecnológico, Celaya 38010, MexicoTecnológico Nacional de México en Celaya (TecNM), Antonio García Cubas, Esquina, Av. Tecnológico, Celaya 38010, MexicoThe condition of the roads where cars circulate is of the utmost importance to ensure that each autonomous or manual car can complete its journey satisfactorily. The existence of potholes, speed bumps, and other irregularities in the pavement can cause car wear and fatal traffic accidents. Therefore, detecting and characterizing these anomalies helps reduce the risk of accidents and damage to the vehicle. However, street images are naturally multivariate, with redundant and substantial information, as well as significantly contaminated measurement noise, making the detection of street anomalies more challenging. In this work, an automatic color image analysis using a deep neural network for the detection of potholes on the road using images taken by a ZED camera is proposed. A lightweight architecture was designed to speed up training and usage. This consists of seven properly connected and synchronized layers. All the pixels of the original image are used without resizing. The classic stride and pooling operations were used to obtain as much information as possible. A database was built using a ZED camera seated on the front of a car. The routes where the photographs were taken are located in the city of Celaya in Guanajuato, Mexico. Seven hundred and fourteen images were manually tagged, several of which contain bumps and potholes. The system was trained with 70% of the database and validated with the remaining 30%. In addition, we propose a database that discriminates between potholes and speed bumps. A precision of 98.13% using 37 convolution filters in a 3 × 3 window was obtained, which improves upon recent state-of-the-art work.https://www.mdpi.com/2076-3417/13/14/8349speed bump detectiondeep neural networkpothole detectionsafe car driving |
| spellingShingle | José-Eleazar Peralta-López Joel-Artemio Morales-Viscaya David Lázaro-Mata Marcos-Jesús Villaseñor-Aguilar Juan Prado-Olivarez Francisco-Javier Pérez-Pinal José-Alfredo Padilla-Medina Juan-José Martínez-Nolasco Alejandro-Israel Barranco-Gutiérrez Speed Bump and Pothole Detection Using Deep Neural Network with Images Captured through ZED Camera Applied Sciences speed bump detection deep neural network pothole detection safe car driving |
| title | Speed Bump and Pothole Detection Using Deep Neural Network with Images Captured through ZED Camera |
| title_full | Speed Bump and Pothole Detection Using Deep Neural Network with Images Captured through ZED Camera |
| title_fullStr | Speed Bump and Pothole Detection Using Deep Neural Network with Images Captured through ZED Camera |
| title_full_unstemmed | Speed Bump and Pothole Detection Using Deep Neural Network with Images Captured through ZED Camera |
| title_short | Speed Bump and Pothole Detection Using Deep Neural Network with Images Captured through ZED Camera |
| title_sort | speed bump and pothole detection using deep neural network with images captured through zed camera |
| topic | speed bump detection deep neural network pothole detection safe car driving |
| url | https://www.mdpi.com/2076-3417/13/14/8349 |
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