A deep learning approach based defect visualization in pulsed thermography

Selvan, Sethu Selvi; Sharath, D.; Menaka, M.; Venkatraman, B.; Udupa, Sathvik; Khandelwal, Tanvi; Mulla, Touqeer; Ittigi, Varun

doi:10.11591/ijai.v11.i3.pp949-960

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…These machine learning approaches classify wood defects by factoring the statistical variations of the defect images to learn about the desired defects with the assistance of several classifiers such as neural networks [59], k-nearest neighbors (k-NN), decision trees and SVM [17]. On the contrary, deep learning has been shown to be highly effective in a wide range of image-based applications, including object detection and identification, facial detection and pattern identification due to their network flexibility in discovering custom defects based on the dataset [60]- [64]. Furthermore, feature extraction for deep learning is embedded in the learning algorithm, where features are extracted in a fully-automated manner, without requiring any intervention from a human expert.…”

Section: Approaches For the Identification Of Timber Defectsmentioning

confidence: 99%

A review of the automated timber defect identification approach

Chun

Hashim²,

Ahmad³

et al. 2023

IJECE

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Timber quality control is undoubtedly a very laborious process in the secondary wood industry. Manual inspections by operators are prone to human error, thereby resulting in poor timber quality inspections and low production volumes. The automation of this process using an automated vision inspection (AVI) system integrated with artificial intelligence appears to be the most plausible approach due to its ease of use and minimal operating costs. This paper provides an overview of previous works on the automated inspection of timber surface defects as well as various machine learning and deep learning approaches that have been implemented for the identification of timber defects. Contemporary algorithms and techniques used in both machine learning and deep learning are discussed and outlined in this review paper. Furthermore, the paper also highlighted the possible limitation of employing both approaches in the identification of the timber defect along with several future directions that may be further explored.

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Section: Approaches For the Identification Of Timber Defectsmentioning

confidence: 99%