PotNet: Pothole detection for autonomous vehicle system using convolutional neural network

Advancement in vision‐based techniques has enabled the autonomous vehicle system (AVS) to understand the driving scene in depth. The capability of autonomous vehicle system to understand the scene, and detecting the specific object depends on the strong feature representation of such objects. Howeve...

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Vydáno v:Electronics letters Ročník 57; číslo 2; s. 53 - 56
Hlavní autoři: Dewangan, Deepak Kumar, Sahu, Satya Prakash
Médium: Journal Article
Jazyk:angličtina
Vydáno: Stevenage John Wiley & Sons, Inc 01.01.2021
Wiley
Témata:
Accuracy
Artificial neural networks
Autonomous vehicles
Autonomy
Cameras
Computer vision and image processing techniques
Control engineering computing
Mobile robots
Neural networks
Optical, image and video signal processing
Prototypes
Road-traffic system control
Roads & highways
Smartphones
Traffic engineering computing
ISSN:0013-5194, 1350-911X
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Shrnutí:Advancement in vision‐based techniques has enabled the autonomous vehicle system (AVS) to understand the driving scene in depth. The capability of autonomous vehicle system to understand the scene, and detecting the specific object depends on the strong feature representation of such objects. However, pothole objects are difficult to identify due to their non‐uniform structure in challenging, and dynamic road environments. Existing approaches have shown limited performance for the precise detection of potholes. The study on the detection of potholes, and intelligent driving behaviour of autonomous vehicle system is little explored in existing articles. Hence, here, an improved prototype model, which is not only truly capable of detecting the potholes but also shows its intelligent driving behaviour when any pothole is detected, is proposed. The prototype is developed using a convolutional neural network with a vision camera to explore, and validates the potential, and autonomy of its driving behaviour in the prepared road environment. The experimental analysis of the proposed model on various performance measures have obtained accuracy, sensitivity, and F‐measure of 99.02%, 99.03%, and 98.33%, respectively, which are comparable with the available state‐of‐art techniques.
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ISSN:0013-5194
1350-911X
DOI:10.1049/ell2.12062