Road Damage Detection and Classification Using Deep Neural Networks (YOLOv4) with Smartphone Images

Faramarzi, Masoud

doi:10.2139/ssrn.3627382

Cited by 7 publications

(2 citation statements)

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“…To validate the reliability and advantages of our self-made crack datasets, we conducted a comparative study using these four model algorithms on existing various open-source UAV pavement crack datasets. Our experiment involved comparing the detection accuracy of our crack datasets with datasets such as UAPD [2], RDD2022 [31], UMSC [19], UAVRoadCrack [21], and CrackForest [32]. We evaluated and compared the accuracy performances of Faster-RCNN, YOLOv5, YOLOv7-tiny, and YOLOv8s after 200 training cycles, as well as Faster-RCNN after 15 rounds.…”

Section: The Results Of Detection Accuracy Under Different Crack Typesmentioning

confidence: 99%

A Pavement Crack Detection and Evaluation Framework for a UAV Inspection System Based on Deep Learning

Chen,

Liu,

Chen

et al. 2024

Applied Sciences

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Existing studies often lack a systematic solution for an Unmanned Aerial Vehicles (UAV) inspection system, which hinders their widespread application in crack detection. To enhance its substantial practicality, this study proposes a formal and systematic framework for UAV inspection systems, specifically designed for automatic crack detection and pavement distress evaluation. The framework integrates UAV data acquisition, deep-learning-based crack identification, and road damage assessment in a comprehensive and orderly manner. Firstly, a flight control strategy is presented, and road crack data are collected using DJI Mini 2 UAV imagery, establishing high-quality UAV crack image datasets with ground truth information. Secondly, a validation and comparison study is conducted to enhance the automatic crack detection capability and provide an appropriate deployment scheme for UAV inspection systems. This study develops automatic crack detection models based on mainstream deep learning algorithms (namely, Faster-RCNN, YOLOv5s, YOLOv7-tiny, and YOLOv8s) in urban road scenarios. The results demonstrate that the Faster-RCNN algorithm achieves the highest accuracy and is suitable for the online data collection of UAV and offline inspection at work stations. Meanwhile, the YOLO models, while slightly lower in accuracy, are the fastest algorithms and are suitable for the lightweight deployment of UAV with online collection and real-time inspection. Quantitative measurement methods for road cracks are presented to assess road damage, which will enhance the application of UAV inspection systems and provide factual evidence for the maintenance decisions made by road authorities.

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Section: The Results Of Detection Accuracy Under Different Crack Typesmentioning

confidence: 99%

A Pavement Crack Detection and Evaluation Framework for a UAV Inspection System Based on Deep Learning

Chen,

Liu,

Chen

et al. 2024

Applied Sciences

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“…The image recognition-based detection method can analyze road-surface conditions over a large area at a low cost by utilizing a deep neural network. As a result of the advancement of image-recognition technology, this approach has gained popularity in recent years [6]. Machine learning techniques are increasingly applied to road crack detection and segmentation [7].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Pavement Crack Detection Based on Artificial Intelligence

Norsuzila Ya’acob,

Mohamad Danial Ikmal Zuraimi,

Amirul Asraf Abdul Rahman

et al. 2024

ARASET

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Pavement as a structural element is closely related to the road traffic system because it is essentially a support structure for the movement of vehicles. Modern technology has attained significant improvements in road durability, quality, and safety through the use of new materials and construction technology. All civil engineering infrastructures have a specific lifespan. The road surface can be the worst, and repair is more costly if the pavement is not maintained. The objective of this research is to spot the damages over the inspected road and detect cracks in a short time, useful to achieve more comprehensive monitoring and assessment of the condition of road pavement using a smartphone equipped with You Only Look Once (YOLO) to reduce maintenance costs. The method uses advanced image processing techniques and uses YOLO to detect cracked pavement in a short time. More specifically, it is based on the latest generation of Deep Neural Network (DNN) algorithms, such as YOLO V5. YOLO V5 is used as a detector for cracked pavement and image processing as an automated pavement distress detector. The obtained result for longitudinal cracks shows that the area of cracks is 1.1682 m2, with a threshold value of 0.91, while the result for transversal cracks shows the area of cracks of 1.9627 m2, with a threshold value of 0.85. Otsu’s method works best under conditions such as low noise level, homogeneous lighting, and higher intra-class variance than an inter-class variance. The outcome of this project is improved recognition accuracy compared to the manual recognition currently used.

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Novel approach for forest road maintenance using smartphone sensor data and deep learning methods

Heidari,

Najafi,

Borges

et al. 2024

International Journal of Forest Engineering

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Road Damage Detection and Classification Using Deep Neural Networks (YOLOv4) with Smartphone Images

Cited by 7 publications

References 0 publications

A Pavement Crack Detection and Evaluation Framework for a UAV Inspection System Based on Deep Learning

A Pavement Crack Detection and Evaluation Framework for a UAV Inspection System Based on Deep Learning

Real-Time Pavement Crack Detection Based on Artificial Intelligence

Novel approach for forest road maintenance using smartphone sensor data and deep learning methods

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