Speed Bump Detection on Roads using Artificial Vision

Ballinas-Hernández, A.L.; Olmos-Pineda, Iván; Olvera-López, J. Arturo

doi:10.13053/rcs-148-9-6

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…In addition to image processing, CV combines artificial intelligence (AI) approaches to derive meaningful information from images and videos [ 54 ]. When merged with Global Positioning Systems (GPS), telescopes, binoculars, closed-circuit television (CCTV), vehicle-mounted video recorders and cameras, and low-cost mobile cameras, image processing-based visual surveillance can significantly increase the efficiency of ARDAD systems [ 18 , 40 , 55 , 56 , 57 ]. Maya et al [ 58 ] proposed a delayed long short-term memory (dLSTM)-based technique that is trained in a normal state and predicts abnormalities depending on the

defined in Equation (2).…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review

Rathee

Bačić

Doborjeh

2023

Sensors

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Recently, there has been a substantial increase in the development of sensor technology. As enabling factors, computer vision (CV) combined with sensor technology have made progress in applications intended to mitigate high rates of fatalities and the costs of traffic-related injuries. Although past surveys and applications of CV have focused on subareas of road hazards, there is yet to be one comprehensive and evidence-based systematic review that investigates CV applications for Automated Road Defect and Anomaly Detection (ARDAD). To present ARDAD’s state-of-the-art, this systematic review is focused on determining the research gaps, challenges, and future implications from selected papers (N = 116) between 2000 and 2023, relying primarily on Scopus and Litmaps services. The survey presents a selection of artefacts, including the most popular open-access datasets (D = 18), research and technology trends that with reported performance can help accelerate the application of rapidly advancing sensor technology in ARDAD and CV. The produced survey artefacts can assist the scientific community in further improving traffic conditions and safety.

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defined in Equation (2).…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review

Rathee

Bačić

Doborjeh

2023

Sensors

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“…This process entails acquiring the image from the surface of the road, which will be used in determining the presence or absence of anomalies by applying image processing techniques. Image processing has become one of the main aspects of automation and safety-related applications in the electronic industries and these images can either be in a digital or analog form [15]. This process of acquiring the image will be carried out using a Pi camera with a resolution of 8 Megapixels, when the vehicle is moving with a speed of 5 to 10 m/s.…”

Section: Road Anomaly Detection A) Image Acquisitionmentioning

confidence: 99%

“…The irregularity detection on the road surface is a task with great challenges. In developing countries, it is very common to find un-marked speedbumps on road surfaces, which reduces the security and stability of self-driving cars [15]. In AVs, speedbump detection and other road anomalies can be achieved by using a number of techniques one of such is the use of mulitple cameras and microcontroller systems [16].…”

Section: Introductionmentioning

confidence: 99%

Development of an Intelligent Road Anomaly Detection System for Autonomous Vehicles

Udah¹,

Suleiman²,

Bala³

et al. 2023

Adv. sci. technol. eng. syst. j.

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“…The proposed CNN model worked successfully by attaining 92.51% recall. In [ 40 ], CNN is proposed for the detection of speed bumps and the authors used an additional image processing technique for the case in which CNN fails to detect speed bumps. Suong et al used Yolov2 for potholes detection by collecting potholes images from Google in [ 41 ] and achieved 82.43% precision and 83.72% recall.…”

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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Speed Bump Detection on Roads using Artificial Vision

Cited by 5 publications

References 21 publications

Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review

Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review

Development of an Intelligent Road Anomaly Detection System for Autonomous Vehicles

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

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