Pothole Detection Using Deep Learning: A Real‐Time and AI‐on‐the‐Edge Perspective

Asad, Muhammad Haroon; Khaliq, Saran; Yousaf, Muhammad Haroon; Ullah, Muhammad Obaid; Ahmad, Afaq

doi:10.1155/2022/9221211

Cited by 58 publications

(17 citation statements)

References 41 publications

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“…The hyperbolic shape of the object detected is characteristic of a buried pipe as described above. Note that YOLOv5 is also useful for various other detection tasks such as bridge health monitoring (Zhuge et al., 2022), pothole detection (Asad et al., 2022), and traffic sign recognition (W. Li et al., 2023).…”

Section: Buried Pipe Detection Methods Using Yolov5mentioning

confidence: 99%

Iterative application of generative adversarial networks for improved buried pipe detection from images obtained by ground‐penetrating radar

Chun

Kato²

2023

Computer aided Civil Eng

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Ground‐penetrating radar (GPR) is widely used to determine the location of buried pipes without excavation, and machine learning has been researched to automatically identify the location of buried pipes from the reflected wave images obtained by GPR. In object detection using machine learning, the accuracy of detection is affected by the quantity and quality of training data, so it is important to expand the training data to improve accuracy. This is especially true in the case of buried pipes that are located underground and whose existence cannot be easily confirmed. Therefore, this study developed a method for increasing training data using you only look once v5 (YOLOv5) and StyleGAN2‐ADA to automate the annotation process. Of particular importance is developing a framework for generating images by generative adversarial networks with an emphasis on images that are challenging to detect buried pipes in YOLOv5 and add them to a training dataset to repeat training recursively, which has greatly improved the detection accuracy. Specifically, F‐values of 0.915, 0.916, and 0.924 were achieved by automatically generating training images step by step from only 500, 1000, and 2000 training images, respectively. These values exceed the F‐value of 0.900, which is obtained from training by manually annotating 15,000 images, a much larger number. In addition, we applied the method to a road in Shizuoka Prefecture, Japan, and confirmed that the method can detect the location of buried pipes with high accuracy on a real road. This method can contribute to labor‐saving training data expansion, which is time‐consuming and costly in practice, and as a result, the method contributes to improving detection accuracy.

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Section: Buried Pipe Detection Methods Using Yolov5mentioning

confidence: 99%

Iterative application of generative adversarial networks for improved buried pipe detection from images obtained by ground‐penetrating radar

Chun

Kato²

2023

Computer aided Civil Eng

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“…Dharnesshkar et al [29] trained a dataset of 1500 Indian road images collected from Coimbatore, Idukki, and Kymily on the YOLOv2, YOLOv3, and YOLOv3 tiny models, achieving mAP(IoU = 0.5) 45.33%, 38.41%, and 49.71%, respectively. Asad et al [30] trained 665 pothole datasets on the YOLOv4 tiny, YOLOv4, and YOLOv5 models, and verified the real-time detection possibility in a low-end embedded system. An OAK-D camera with a single main board (Raspberry Pi) was used to detect potholes.…”

Section: Pothole Object Detectionmentioning

confidence: 96%

Image-Based Pothole Detection Using Multi-Scale Feature Network and Risk Assessment

Heo

Choi

Kim³

et al. 2023

Electronics

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Potholes on road surfaces pose a serious hazard to vehicles and passengers due to the difficulty detecting them and the short response time. Therefore, many government agencies are applying various pothole-detection algorithms for road maintenance. However, current methods based on object detection are unclear in terms of real-time detection when using low-spec hardware systems. In this study, the SPFPN-YOLOv4 tiny was developed by combining spatial pyramid pooling and feature pyramid network with CSPDarknet53-tiny. A total of 2665 datasets were obtained via data augmentation, such as gamma regulation, horizontal flip, and scaling to compensate for the lack of data, and were divided into training, validation, and test of 70%, 20%, and 10% ratios, respectively. As a result of the comparison of YOLOv2, YOLOv3, YOLOv4 tiny, and SPFPN-YOLOv4 tiny, the SPFPN-YOLOv4 tiny showed approximately 2–5% performance improvement in the mean average precision (intersection over union = 0.5). In addition, the risk assessment based on the proposed SPFPN-YOLOv4 tiny was calculated by comparing the tire contact patch size with pothole size by applying the pinhole camera and distance estimation equation. In conclusion, we developed an end-to-end algorithm that can detect potholes and classify the risks in real-time using 2D pothole images.

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“…Precision is a measure of the accuracy of the model in detecting road anomalies. This metric is evaluated using Equation ( 6) [47].…”

Section: Model Performance Evaluationmentioning

confidence: 99%

“…Recall is a measure of the model performance in detecting all anomalies in the images. This metric is evaluated using Equation ( 7) [47].…”

Section: Model Performance Evaluationmentioning

confidence: 99%

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Performance Evaluation of You Only Look Once v4 in Road Anomaly Detection and Visual Simultaneous Localisation and Mapping for Autonomous Vehicles

Bala,

Adeshina,

Aibinu

2023

WEVJ

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The proliferation of autonomous vehicles (AVs) emphasises the pressing need to navigate challenging road networks riddled with anomalies like unapproved speed bumps, potholes, and other hazardous conditions, particularly in low- and middle-income countries. These anomalies not only contribute to driving stress, vehicle damage, and financial implications for users but also elevate the risk of accidents. A significant hurdle for AV deployment is the vehicle’s environmental awareness and the capacity to localise effectively without excessive dependence on pre-defined maps in dynamically evolving contexts. Addressing this overarching challenge, this paper introduces a specialised deep learning model, leveraging YOLO v4, which profiles road surfaces by pinpointing defects, demonstrating a mean average precision (mAP@0.5) of 95.34%. Concurrently, a comprehensive solution—RA-SLAM, which is an enhanced Visual Simultaneous Localisation and Mapping (V-SLAM) mechanism for road scene modeling, integrated with the YOLO v4 algorithm—was developed. This approach precisely detects road anomalies, further refining V-SLAM through a keypoint aggregation algorithm. Collectively, these advancements underscore the potential for a holistic integration into AV’s intelligent navigation systems, ensuring safer and more efficient traversal across intricate road terrains.

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Pothole Detection Using Deep Learning: A Real‐Time and AI‐on‐the‐Edge Perspective

Cited by 58 publications

References 41 publications

Iterative application of generative adversarial networks for improved buried pipe detection from images obtained by ground‐penetrating radar

Iterative application of generative adversarial networks for improved buried pipe detection from images obtained by ground‐penetrating radar

Image-Based Pothole Detection Using Multi-Scale Feature Network and Risk Assessment

Performance Evaluation of You Only Look Once v4 in Road Anomaly Detection and Visual Simultaneous Localisation and Mapping for Autonomous Vehicles

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