Pothole Detection using Machine Learning

Song, Hyunwoo; Baek, Kihoon; Byun, Yung-Cheol

doi:10.14257/astl.2018.150.35

Cited by 38 publications

(7 citation statements)

References 3 publications

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“…Faster R-CNN, a two-stage pothole detector, has gotten little attention in the scientific community. They can achieve high accuracy while still moving at a tolerable pace because of their moderate detecting speed [23].…”

Section: Approach Challengesmentioning

confidence: 99%

Pothole and Plain Road Classification Using Adaptive Mutation Dipper Throated Optimization and Transfer Learning for Self Driving Cars

et al. 2022

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Self-driving car plays a crucial role in implementing traffic intelligence. Road smoothness in front of self-driving cars has a significant impact on the car's driving safety and comfort. Having potholes on the road may lead to several problems, including car damage and the occurrence of collisions. Therefore, self-driving cars should be able to change their driving behavior based on the real-time detection of road potholes. Various methods are followed to address this problem, including reporting to authorities, employing vibration-based sensors, and 3D laser imaging. However, limitations, such as expensive setup costs and the danger of discovery, affected these methods. Therefore, it is necessary to automate the process of potholes identification with sufficient precision and speed. A novel method based on adaptive mutation and dipper throated optimization (AMDTO) for feature selection and optimization of the random forest (RF) classifier is presented in this paper. In addition, we propose a new adaptive method for dataset balancing, referred to as optimized hashing SMOTE, to boost the performance of the optimized model. Data on potholes in different weather conditions and circumstances were collected and augmented before training the proposed model. The effectiveness of the proposed method is shown in experiments in classifying road potholes accurately. Eleven feature selection methods, including WOA, GWO, and PSO, and three machine learning classifiers were included in the conducted experiments to measure the superiority of the proposed method. The proposed method, AMDTO+RF, achieved a pothole classification accuracy of (99.795%), which outperforms the accuracy achieved by the other approaches, WOA+RF of 97.5%, GWO+RF of 98.6%, PSO+RF of 98.1%, and transfer learning approaches, AlexNet of 86.8%, VGG-19 of 87.3%, GoogLeNet of 90.4%, and ResNet-50 of 93.8%. In addition, an in-depth statistical analysis is performed on the recorded results to study the significance and stability of the proposed method.

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Section: Approach Challengesmentioning

confidence: 99%

Pothole and Plain Road Classification Using Adaptive Mutation Dipper Throated Optimization and Transfer Learning for Self Driving Cars

et al. 2022

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“…Prashant et al 4 proposed a system called Nericell for rich monitoring of road and traffic conditions using smartphones equipped with multiple sensors (GPS, accelerometer, microphone) and communication radios. Song et al 5 proposed a Cost Effective and Computationally Efficient (CECE) model to categorize potholes using smartphone sensors by processing the data. They utilized Transfer Learning and Inception V3 to achieve flexible ways of application.…”

Section: Related Workmentioning

confidence: 99%

DeepBus: Machine learning based real time pothole detection system for smart transportation using IoT

Bansal

Mittal

Ahuja

et al. 2020

Internet Technology Letters

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Road related accidents have always been a nuisance to drivers and pedestrians alike.Every year countless accidents and deaths occur due to potholes which could have been preventable if there had been a prior warning or if the civic authorities were able to repair these potholes in time. This paper proposes a machine learning based pothole detection system called DeepBus for real time identification of surface irregularities on roads using Internet of Things (IoT). DeepBus uses IoT sensors to detect potholes in real time while an end user is driving vehicles on the road. The location of these potholes would be available on a centrally hosted map which can be accessed by both end users and civic authorities. Thus, it would serve as a warning system to all users as well as a database of potholes with thier locations to the authorities for quick repair and action. We have compared the performance of various machine learning models (Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes, Decision Tree, Random Forest and Ensemble Voting) based on different parameters (Accuracy, F-score, Precision and Recall) and identified that Random Forest is the best model for pothole detection.

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“…A. Kwang et al [6] detected potholes by using smartphone camera and image classification method based on the deep convolutional neural network models and obtained the classification accuracy rate of 97% using coloured images and 97.5% using grayscale images. S. Hyunwoo et al (2018) [7] proposed a way to implement detection of pothole using a smartphone, and classification is performed using Transfer Learning. 70% data (70 instances) is used for training and 30 % data (30 instances) is used for testing.…”

Section: Related Workmentioning

confidence: 99%

Road Pothole Detection using Deep Learning Classifiers

Arjapure¹,

Kalbande²

2020

IJRTE

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Pothole is one of the major types of defects frequently found on the road whose assessment is necessary to process. It is one of the important reason of accidents on the road along with the wear and tear of vehicles. Road defects assessment is to be done through defects data collection and processing of this collected data. Currently, using various types of imaging systems data collection is near about becomes automated but an assessment of defects from collected data is still manual. Manual classification and evaluation of potholes are expensive, labour-intensive, time-consuming and thus slows down the overall road maintenance process. This paper describe a method for classification and detection of the potholes on road images using convolutional neural networks which are deep learning algorithms. In the proposed system we used convolutional neural networks based approach with pre-trained models to classify given input images into a pothole and non-pothole categories. The method was implemented in python using OpenCV library under windows and colab environment, trained on 722 and tested on 116 raw images. The results are evaluated and compared for convolutional neural networks and various seven pre-trained models through accuracy, precision and recall metrics. The results show that pre-trained models InseptionResNetV2 and DenseNet201 can detect potholes on road images with reasonably good accuracy of 89.66%.

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Pothole Detection using Machine Learning

Cited by 38 publications

References 3 publications

Pothole and Plain Road Classification Using Adaptive Mutation Dipper Throated Optimization and Transfer Learning for Self Driving Cars

Pothole and Plain Road Classification Using Adaptive Mutation Dipper Throated Optimization and Transfer Learning for Self Driving Cars

DeepBus: Machine learning based real time pothole detection system for smart transportation using IoT

Road Pothole Detection using Deep Learning Classifiers

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