Survey on performance of deep learning models for detecting road damages using multiple dashcam image resources

Cao, Minh-Tu; Tran, Quoc‐Viet; Nguyen, Ngoc‐Mai; Chang, Kuan-Tsung

doi:10.1016/j.aei.2020.101182

Cited by 93 publications

(41 citation statements)

References 54 publications

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“…A machine learningbased approach using least squares support vector machine and neural network with steerable filter-based feature extraction has been proposed in [18]. Maeda et al [19] and Cao et al [20] recently put forward deep neural network-based approaches for recognizing asphalt pavement defects including potholes.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

2021

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During the phase of periodic asphalt pavement survey, patched and unpatched potholes need to be accurately detected. This study proposes and verifies a computer vision-based approach for automatically distinguishing patched and unpatched potholes. Using two-dimensional images, patched and unpatched potholes may have similar shapes. Therefore, this study relies on image texture descriptors to delineate these two objects of interest. The texture descriptors of statistical measurement of color channels, the gray-level cooccurrence matrix, and the local ternary pattern are used to extract texture information from image samples of asphalt pavement roads. To construct a classification model based on the extracted texture-based dataset, this study proposes and validates an integration of the Support Vector Machine Classification (SVC) and the Forensic-Based Investigation (FBI) metaheuristic. The SVC is used to generalize a classification boundary that separates the input data into two class labels of patched and unpatched potholes. To optimize the SVC performance, the FBI algorithm is utilized to fine-tune the SVC hyperparameters. To establish the hybrid FBI-SVC framework, an image dataset consisting of 600 samples has been collected. The experiment supported by the Wilcoxon signed-rank test demonstrates that the proposed computer vision is highly suitable for the task of interest with a classification accuracy rate = 94.833%.

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Section: Introductionmentioning

confidence: 99%

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

2021

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“…Faster R-CNN ResNet-101 is a region-based CNN. MobileNet is notoriously faster but less accurate than Faster R-CNN ResNet 101 [53,54]. Indeed, MobileNet is designed for efficient inference in various mobile and embedded vision applications.…”

Section: Exchanged Data Trafficmentioning

confidence: 99%

Virtualizing AI at the Distributed Edge towards Intelligent IoT Applications

Campolo

Genovese

Molinaro

2021

JSAN

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Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

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“…The rate of precision and recall for SV1 is 45.8% and 45.8%, for SV2 is 67.4% and 51.2%, and for LM1 is 89.8% and 92.8%, while the LM2 model is capable of detecting potholes in real time. In [ 24 ], the real-time detection of five classes of road damages from images i.e., longitudinal crack, horizontal crack, alligator damage, pothole-related crack, and line blurring is accomplished by using a single-shot multibox detector (SSD) and faster region-based convolutional neural networks (R-CNN) with Inception V2 and ResNet. The results show that the achieved accuracy rate of 0.5306 for Inception ResNet V2 is better than other approaches.…”

Section: Introductionmentioning

confidence: 99%

Edge AI‐Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning

Bibi

Saeed

Zeb

et al. 2021

Computational Intelligence and Neuroscience

142

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Road surface defects are crucial problems for safe and smooth traffic flow. Due to climate changes, low quality of construction material, large flow of traffic, and heavy vehicles, road surface anomalies are increasing rapidly. Detection and repairing of these defects are necessary for the safety of drivers, passengers, and vehicles from mechanical faults. In this modern era, autonomous vehicles are an active research area that controls itself with the help of in-vehicle sensors without human commands, especially after the emergence of deep learning (DNN) techniques. A combination of sensors and DNN techniques can be useful for unmanned vehicles for the perception of their surroundings for the detection of tracks and obstacles for smooth traveling based on the deployment of artificial intelligence in vehicles. One of the biggest challenges for autonomous vehicles is to avoid the critical road defects that may lead to dangerous situations. To solve the accident issues and share emergency information, the Intelligent Transportation System (ITS) introduced the concept of vehicular network termed as vehicular ad hoc network (VANET) for achieving security and safety in a traffic flow. A novel mechanism is proposed for the automatic detection of road anomalies by autonomous vehicles and providing road information to upcoming vehicles based on Edge AI and VANET. Road images captured via camera and deployment of the trained model for road anomaly detection in a vehicle could help to reduce the accident rate and risk of hazards on poor road conditions. The techniques Residual Convolutional Neural Network (ResNet-18) and Visual Geometry Group (VGG-11) are applied for the automatic detection and classification of the road with anomalies such as a pothole, bump, crack, and plain roads without anomalies using the dataset from different online sources. The results show that the applied models performed well than other techniques used for road anomalies identification.

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Survey on performance of deep learning models for detecting road damages using multiple dashcam image resources

Cited by 93 publications

References 54 publications

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

Virtualizing AI at the Distributed Edge towards Intelligent IoT Applications

Edge AI‐Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning

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