Enhanced pothole detection system using YOLOX algorithm
Abstract The road is the most commonly used means of transportation and serves as a country’s arteries, so it is extremely important to keep the roads in good condition. Potholes that happen to appear in the road must be repaired to keep the road in good condition. Spotting potholes on the road is d...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2022-08-01
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| Series: | Autonomous Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s43684-022-00037-z |
| _version_ | 1828149061392269312 |
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| author | Mohan Prakash B Sriharipriya K.C |
| author_facet | Mohan Prakash B Sriharipriya K.C |
| author_sort | Mohan Prakash B |
| collection | DOAJ |
| description | Abstract The road is the most commonly used means of transportation and serves as a country’s arteries, so it is extremely important to keep the roads in good condition. Potholes that happen to appear in the road must be repaired to keep the road in good condition. Spotting potholes on the road is difficult, especially in a country like India where roads stretch millions of kilometres across the country. Therefore, there is a need to automate the identification of potholes with high speed and real-time precision. YOLOX is an object detection algorithm and our main goal of this article is to train and analyse the YOLOX model for pothole detection. The YOLOX model is trained with a pothole dataset and the results obtained are analysed by calculating the accuracy, recall and size of the model which is then compared to other YOLO algorithms. The experimental results in this article show that the YOLOX-Nano model predicts potholes with higher accuracy compared to other models while having low computational costs. We were able to achieve an Average Precision (AP) value of 85.6% from training the model and the total size of the model is 7.22 MB. The pothole detection capabilities of the newly developed YOLOX algorithm have never been tested before and this paper is one of the first to detect potholes using the YOLOX object detection algorithm. The research conducted in this paper will help reduce costs and increase the speed of pothole identification and will be of great help in road maintenance. |
| first_indexed | 2024-04-11T21:24:57Z |
| format | Article |
| id | doaj.art-60d852f7a3b24eaa9b4c9bc0278cad92 |
| institution | Directory Open Access Journal |
| issn | 2730-616X |
| language | English |
| last_indexed | 2024-04-11T21:24:57Z |
| publishDate | 2022-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Autonomous Intelligent Systems |
| spelling | doaj.art-60d852f7a3b24eaa9b4c9bc0278cad922022-12-22T04:02:28ZengSpringerAutonomous Intelligent Systems2730-616X2022-08-012111610.1007/s43684-022-00037-zEnhanced pothole detection system using YOLOX algorithmMohan Prakash B0Sriharipriya K.C1SENSE, VIT UniversitySENSE, VIT UniversityAbstract The road is the most commonly used means of transportation and serves as a country’s arteries, so it is extremely important to keep the roads in good condition. Potholes that happen to appear in the road must be repaired to keep the road in good condition. Spotting potholes on the road is difficult, especially in a country like India where roads stretch millions of kilometres across the country. Therefore, there is a need to automate the identification of potholes with high speed and real-time precision. YOLOX is an object detection algorithm and our main goal of this article is to train and analyse the YOLOX model for pothole detection. The YOLOX model is trained with a pothole dataset and the results obtained are analysed by calculating the accuracy, recall and size of the model which is then compared to other YOLO algorithms. The experimental results in this article show that the YOLOX-Nano model predicts potholes with higher accuracy compared to other models while having low computational costs. We were able to achieve an Average Precision (AP) value of 85.6% from training the model and the total size of the model is 7.22 MB. The pothole detection capabilities of the newly developed YOLOX algorithm have never been tested before and this paper is one of the first to detect potholes using the YOLOX object detection algorithm. The research conducted in this paper will help reduce costs and increase the speed of pothole identification and will be of great help in road maintenance.https://doi.org/10.1007/s43684-022-00037-zYOLOYOLOXObject detectionPothole detectionMachine learning |
| spellingShingle | Mohan Prakash B Sriharipriya K.C Enhanced pothole detection system using YOLOX algorithm Autonomous Intelligent Systems YOLO YOLOX Object detection Pothole detection Machine learning |
| title | Enhanced pothole detection system using YOLOX algorithm |
| title_full | Enhanced pothole detection system using YOLOX algorithm |
| title_fullStr | Enhanced pothole detection system using YOLOX algorithm |
| title_full_unstemmed | Enhanced pothole detection system using YOLOX algorithm |
| title_short | Enhanced pothole detection system using YOLOX algorithm |
| title_sort | enhanced pothole detection system using yolox algorithm |
| topic | YOLO YOLOX Object detection Pothole detection Machine learning |
| url | https://doi.org/10.1007/s43684-022-00037-z |
| work_keys_str_mv | AT mohanprakashb enhancedpotholedetectionsystemusingyoloxalgorithm AT sriharipriyakc enhancedpotholedetectionsystemusingyoloxalgorithm |