Front-View Vehicle Damage Detection using Roadway Surveillance Camera Images

Balcı, Burak; Artan, Yusuf; Alkan, Bensu; Elihoş, Alperen

doi:10.5220/0007724601930198

Cited by 11 publications

(3 citation statements)

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“…By looking deeply, we come to understand that the PANet [42] model indicates a very similar performance to the texture descriptor [8] model, and the VGG model [1] demonstrates an almost alike consequence to the combined feature (YOLOv3) [43] model. e differences between the obtained values of recall and precision criteria from the FCOMB [12] model and the suggested model are great numbers equal to 34% and 33%, respectively.…”

Section: Resultsmentioning

confidence: 89%

“…As it is clearly indicated, the proposed architecture gained the best results among all the eight other models. Moreover, the FCOMB [12] and VGG [1] strategies have the worst outcome among all the other techniques in terms of all evaluation criteria while the texture descriptor [8] pipeline is the second best model.…”

Section: Resultsmentioning

confidence: 99%

“…Detecting the damaged parts of the outer surface in various kinds of cars has received great attention in the eld of machine vision in recent years, and many frameworks have been proposed for reducing the claim leak issue [2,12,13].…”

Section: Introductionmentioning

confidence: 99%

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Recognizing the Damaged Surface Parts of Cars in the Real Scene Using a Deep Learning Framework

Parhizkar

Amirfakhrian

2022

Mathematical Problems in Engineering

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Automatically recognizing the damaged surface parts of cars can noticeably diminish the cost of processing premium assertion that leads to providing contentment for vehicle users. This recognition task can be conducted using some machine learning (ML) strategies. Deep learning (DL) models as subsets of ML have indicated remarkable potential in object detection and recognition tasks. In this study, an automated recognition of the damaged surface parts of cars in the real scene is suggested that is based on a two-path convolutional neural network (CNN). Our strategy utilizes a ResNet-50 at the beginning of each route to explore low-level features efficiently. Moreover, we proposed new mReLU and inception blocks in each route that are responsible for extracting high-level visual features. The experimental results proved the suggested model obtained high performance in comparison to some state-of-the-art frameworks.

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Section: Resultsmentioning

confidence: 89%

Section: Resultsmentioning

confidence: 99%