Pothole Detection and Dimension Estimation System using Deep Learning (YOLO) and Image Processing

Chitale, Pranjal; Kekre, Kaustubh Y.; Shenai, Hrishikesh; Karani, Reena; Gala, Jay

doi:10.1109/ivcnz51579.2020.9290547

Cited by 31 publications

(8 citation statements)

References 8 publications

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“…Hand-held mobile cameras, as used by [47,[50][51][52][53] have significant utilization in road surface distress detection. The top-view camera setup provides a better ground sampling distance than the wide-view setup, while the wide-view is much quicker as it covers more area per image.…”

Section: Data Acquisition Processmentioning

confidence: 99%

An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment

Qureshi

Hassan

McKeever

et al. 2022

Sensors

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Road pavement condition assessment is essential for maintenance, asset management, and budgeting for pavement infrastructure. Countries allocate a substantial annual budget to maintain and improve local, regional, and national highways. Pavement condition is assessed by measuring several pavement characteristics such as roughness, surface skid resistance, pavement strength, deflection, and visual surface distresses. Visual inspection identifies and quantifies surface distresses, and the condition is assessed using standard rating scales. This paper critically analyzes the research trends in the academic literature, professional practices and current commercial solutions for surface condition ratings by civil authorities. We observe that various surface condition rating systems exist, and each uses its own defined subset of pavement characteristics to evaluate pavement conditions. It is noted that automated visual sensing systems using intelligent algorithms can help reduce the cost and time required for assessing the condition of pavement infrastructure, especially for local and regional road networks. However, environmental factors, pavement types, and image collection devices are significant in this domain and lead to challenging variations. Commercial solutions for automatic pavement assessment with certain limitations exist. The topic is also a focus of academic research. More recently, academic research has pivoted toward deep learning, given that image data is now available in some form. However, research to automate pavement distress assessment often focuses on the regional pavement condition assessment standard that a country or state follows. We observe that the criteria a region adopts to make the evaluation depends on factors such as pavement construction type, type of road network in the area, flow and traffic, environmental conditions, and region’s economic situation. We summarized a list of publicly available datasets for distress detection and pavement condition assessment. We listed approaches focusing on crack segmentation and methods concentrating on distress detection and identification using object detection and classification. We segregated the recent academic literature in terms of the camera’s view and the dataset used, the year and country in which the work was published, the F1 score, and the architecture type. It is observed that the literature tends to focus more on distress identification (“presence/absence” detection) but less on distress quantification, which is essential for developing approaches for automated pavement rating.

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Section: Data Acquisition Processmentioning

confidence: 99%

An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment

Qureshi

Hassan

McKeever

et al. 2022

Sensors

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“…Compared to v2, the ability to detect small objects, accuracy and real-time ability have been further improved. A lot of works has been done extensively using the YOLOv3 algorithm [30][31][32][33] to design an object detection system that can be used to detect potholes from a video feed. Dharneeshkar et al [30] proposed a YOLOv3-based pothole detection system and the results were obtained and analyzed against YOLOv2 and YOLOv3-tiny.…”

Section: Related Workmentioning

confidence: 99%

“…The researchers also improved their pothole detection mechanism by using the YOLOv4 algorithm for various object detection applications. Various pothole detection mechanisms using YOLOv4 [32,35,36] have been proposed. Authors Sung-sick et al [36] used different modules from YOLOv4 and YOLOv5s for pothole detecting applications.…”

Section: Related Workmentioning

confidence: 99%

Enhanced pothole detection system using YOLOX algorithm

K.C

2022

Auton. Intell. Syst.

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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.

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“…YOLOv4 (Bochkovskiy et al, 2020) is an updated version of YOLOv3 (Chitale et al, 2020) with a few models' architectural changes and increased overall performance. The feature pyramid network (FPN) structure is maintained as the training approach in YOLOv4.…”

Section: Yolov4mentioning

confidence: 99%

Road Pothole Detection Using Unmanned Aerial Vehicle Imagery and Deep Learning Technique

2022

ZJPAS

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Potholes are considered the main factor for road defects, which leads to road status deterioration, which, consequently will lead to increased road accidents. The first step in road maintenance is to inspect the road surface and then accurately detect potholes. However, manually identifying them is costly and time-consuming. In this study, unmanned aerial vehicle (UAV) imagery was used to create orthophotos of the roads and, using deep learning methods, potholes were detected. The used deep learning method in this study is the "you only look once" (YOLO) algorithm. YOLO is one of the "deep learning-based approaches" to detecting objects and is a single-stage network which requires only one forward propagation across the neural network and focuses on the entire image. The fourth version of YOLO is YOLOV4, which has two different architectures (YOLOv4 and YOLOv4-tiny). Two roads were chosen as the study areas, and to generate the orthophotos of the roads, UAV was used to acquire images. To train both methods in the process of detecting potholes using deep learning, 5300 images were used, 90% used for training and 10% applied for testing. The two used architectures were trained for 6000 iterations. Both methods were evaluated based on the average loss, mean average precision (mAP), and training and testing time. The results showed that the (mAP) values for YOLOv4 and YOLOv4-tiny were 91. 2% and 85.7%, respectively. At the end of the 6000 iterations, the average loss for YOLOv4 is 0.30% and for YOLOv4-tiny is 0.34%. In the training process, YOLOv4 needs 29 seconds for each iteration, while YOLOv4-tiny requires only 8 seconds. In the test process, YOLOv4-tiny is faster at detecting potholes than YOLOv4. The approaches were tested on orthophotos created by processing UAV photos. When comparing the detection of both architectures with visual detection, the results showed that YOLOv4 was able to detect most of the potholes on roads, but YOLOv4-tiny detected a lower number of potholes.

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Pothole Detection and Dimension Estimation System using Deep Learning (YOLO) and Image Processing

Cited by 31 publications

References 8 publications

An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment

An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment

Enhanced pothole detection system using YOLOX algorithm

Road Pothole Detection Using Unmanned Aerial Vehicle Imagery and Deep Learning Technique

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