Detecting and Localizing Dents on Vehicle Bodies Using Region-Based Convolutional Neural Network

Park, Sung Hyun; Tjolleng, Amir; Chang, Joonho; Cha, Myeongsup; Park, Jong-Cheol; Jung, Kihyo

doi:10.3390/app10041250

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…A region-based convolutional neural network (R-CNN) is used for dent localization on a car surface that could be formed during the manufacturing process [43]. The accuracy of the dent detection is very high, which contributes to producing vehicles with high quality, because of the improved inspection process.…”

Section: Dineva Et Al Propose a New Methodology For Multi-label Classification At The Diagnosis Of Multiple Faults Occurring In Electricamentioning

confidence: 99%

Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice

2021

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The paper presents an analysis and summary of the current research state concerning the application of machine learning and fuzzy logic for solving problems in electronics. The investigated domain is conceptualized with aim the achievements, trending topics and future research directions to be outlined. The applied research methodology includes a bibliographic approach in combination with a detailed examination of 66 selected papers. The findings reveal the gradually increasing interest over the last 10 years in the machine learning and fuzzy logic techniques for modeling, implementing and improving different hardware-based intelligent systems.

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Section: Dineva Et Al Propose a New Methodology For Multi-label Classification At The Diagnosis Of Multiple Faults Occurring In Electricamentioning

confidence: 99%

Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice

2021

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“…In [7] a Mask R-CNN [8] was used to detect and segment pictures of aircraft dents with average IoU of 36.54%. The use of R-CNN was also tested for the sole detection of car dents [9]. In this case light bands were projected on the surface to highlight its deformations.…”

Section: Previous Workmentioning

confidence: 99%

Automatic Segmentation of Aircraft Dents in Point Clouds (SAE Paper 2022-01-0022)

Lafiosca¹,

Fan²,

Avdelidis³

2022

SAE Technical Paper Series

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Dents on the aircraft skin are frequent and may easily go undetected during airworthiness checks, as their inspection process is tedious and extremely subject to human factors and environmental conditions. Nowadays, 3D scanning technologies are being proposed for more reliable, human-independent measurements, yet the process of inspection and reporting remains laborious and time consuming because data acquisition and validation are still carried out by the engineer. For full automation of dent inspection, the acquired point cloud data must be analysed via a reliable segmentation algorithm, releasing humans from the search and evaluation of damage. This paper reports on two developments towards automated dent inspection. The first is a method to generate a synthetic dataset of dented surfaces to train a fully convolutional neural network.The training of machine learning algorithms needs a substantial volume of dent data, which is not readily available. Dents are thus simulated in random positions and shapes, within criteria and definitions of a Boeing 737 structural repair manual. The noise distribution from the scanning apparatus is then added to reflect the complete process of 3D point acquisition on the training. The second proposition is a surface fitting strategy to convert 3D point clouds to 2.5D. This allows higher resolution point clouds to be processed with a small amount of memory compared with state-of-the-art methods involving 3D sampling approaches. Simulations with available ground truth data show that the proposed technique reaches an intersection-over-union of over 80%. Experiments over dent samples prove an effective detection of dents with a speed of over 500 000 points per second.

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“…In this case the defect spectrum is simplified to concave and convex defects, such as dents and bulges. A further camera-based painted-body inspection system utilizing deep learning is proposed in (Park et al, 2020), who utilize LED mach bands and region-based convolutional neural networks (CNNs) in order to detect dents on painted body panels. A further example, this time reverting from the newer concept of raw camera setups to the established principle of optical deflectometry, is given with (Zhou et al, 2020), who couple a custom deflectometric setup with a deep learning backend for paint defect detection.…”

Section: Paint Shop Inspectionmentioning

confidence: 99%

Review on Photogrammetric Surface Inspection in Automotive Production

Hödel

Hoegner

Stilla

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. When purchasing a premium car for a substantial sum, first impressions count. Key to that first impression is a flawless exterior appearance, something self-explanatory for the customer, but a far greater challenge for production than one might initially assume. Fortunately, photogrammetric technologies and evaluation methods are enabling an ever greater degree of oversight in the form of comprehensive quality data at different automotive production stages, namely stamping, welding, painting and finishing. A drawback lies in the challenging production environment, which complicates inline integratability of certain technologies. In recent years, machine vision and deep learning have been applied to photogrammetric surface inspection with ever increasing success. Given comprehensive surface quality information throughout the entire production chain, production parameters can be dialed in ever tighter in a data-driven fashion, leading to a sustainable increase in quality. This paper provides a review of current and potential contributions of photogrammetry to this end, discussing several recent advances in research along the way. Particular emphasis will be placed on early production stages, as well as the application of machine vision and deep learning to this challenging task. An outline for further research conducted by the authors will conclude this paper.

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Detecting and Localizing Dents on Vehicle Bodies Using Region-Based Convolutional Neural Network

Cited by 7 publications

References 21 publications

Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice

Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice

Automatic Segmentation of Aircraft Dents in Point Clouds (SAE Paper 2022-01-0022)

Review on Photogrammetric Surface Inspection in Automotive Production

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