Vision based inspection system for leather surface defect detection using fast convergence particle swarm optimization ensemble classifier approach

Jawahar, Malathy; Babu, N. K. Chandra; Vani, K.; Anbarasi, L. Jani; Geetha, S.

doi:10.1007/s11042-020-09727-3

Cited by 24 publications

(8 citation statements)

References 54 publications

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“…To date, the literature that carried out the automatic classification or segmentation tasks on the leather pieces is yet limited [1][2][3]. Besides, the experimental data are varied and hence it is difficult to make a fair test of performance to verify the effectiveness of the proposed methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Classification System for Tick-Bite Defect on Leather

Gan

Yau

Liong

et al. 2022

Mathematical Problems in Engineering

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Natural leather is a durable, breathable, stretchable, and pliable material that comes in various styles, colors, finishes, and prices. It is an ideal raw material to manufacture luxury products such as shoes, dresses, and luggage. The leather will be categorized into different grades that are determined by visual appearance, softness, and natural defects. This grading process requires a manual visual inspection from experienced experts to ensure proper quality assurance and quality control. To facilitate the inspection process, this paper introduces an efficient automated defect classification framework that is capable to evaluate if the sample patches contain defective segments. A six-step preprocessing procedure is introduced to enhance the quality of the leather image in terms of visibility and to preserve important features representation. Then, multiple classifiers are utilized to differentiate between defective and nondefective leather patches. The proposed framework is capable to generate a classification accuracy rate of 94% from a collection of samples of 1600 pieces of calf leather patches.

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Section: Literature Reviewmentioning

confidence: 99%

Automated Classification System for Tick-Bite Defect on Leather

Gan

Yau

Liong

et al. 2022

Mathematical Problems in Engineering

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“…Since dispersed sensor node optimization is not performed in Agarwal and Kishor, 43 the cost of node deployment will increase significantly. In Jawahar et al, 44 the authors have discussed in detail the implementation details of bio‐inspired PSO algorithm.…”

Section: Related Workmentioning

confidence: 99%

Swarm intelligence‐based optimal device deployment in heterogeneous Internet of Things networks for wind farm application

Anbarasi

Prassanna

et al. 2021

Int J Communication

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Summary Internet of Things (IoT) is becoming a suitable surveillance system in modern power grid, right from power generation to distribution. In the case of wind power generation application, the entire wind farm is considered completely monitored using edge devices known as IoT devices. Device deployment is a major obstacle that can be addressed for this application as unfair device deployment can lead to connectivity issue. This study has two main objectives. The first objective is to determine the optimal number of IoT devices that need to be place in the potential key elements of the wind turbines/targets for coverage assurance. The second objective is to determine the optimal number of relay devices. Spacing wind turbines several hundred meters apart brings the connectivity as a second critical issue, which can be handling by placing optimal number of relay devices. Hence, the focus of this study is to address connected target coverage problem in optimal device placement of heterogeneous IoT network for wind farm monitoring. This paper discusses in detail the swarm intelligence technique known as particle swarm optimization (PSO) to solve the connectivity issue and to minimize the cost of devices deployment. Additionally, a hybrid swarm intelligence model called hybrid PSO‐ACO (HPACO) algorithm is proposed. The main goal of this hybrid model is to reduce the number of relay devices, minimize deployment cost, and to improve network connectivity. The performance of both PSO and HPACO algorithms are validating by comparing the extensive simulation results with ant colony optimizations (ACO) algorithm and other approximation algorithm.

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“…The fitness function plays a key role in defect detection, requiring the ability to differentiate between defective and nondefective areas and identify various types of defects [ 24 ]. For instance, in a study on leather defect detection, a modified fitness function using selective-band Shannon entropy improved segmentation efficiency [ 25 ]. The velocity setting also influences algorithm convergence and search efficiency [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Defects Prediction Method for Radiographic Images Based on Random PSO Using Regional Fluctuation Sensitivity

Shang

Chen

et al. 2023

Sensors

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This paper presents an advanced methodology for defect prediction in radiographic images, predicated on a refined particle swarm optimization (PSO) algorithm with an emphasis on fluctuation sensitivity. Conventional PSO models with stable velocity are often beleaguered with challenges in precisely pinpointing defect regions in radiographic images, attributable to the lack of a defect-centric approach and the propensity for premature convergence. The proposed fluctuation-sensitive particle swarm optimization (FS-PSO) model, distinguished by an approximate 40% increase in particle entrapment within defect areas and an expedited convergence rate, necessitates a maximal additional time consumption of only 2.28%. The model, also characterized by reduced chaotic swarm movement, enhances efficiency through the modulation of movement intensity concomitant with the escalation in swarm size. The FS-PSO algorithm’s performance was rigorously evaluated via a series of simulations and practical blade experiments. The empirical findings evince that the FS-PSO model substantially outperforms the conventional stable velocity model, particularly in terms of shape retention in defect extraction.

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Vision based inspection system for leather surface defect detection using fast convergence particle swarm optimization ensemble classifier approach

Cited by 24 publications

References 54 publications

Automated Classification System for Tick-Bite Defect on Leather

Automated Classification System for Tick-Bite Defect on Leather

Swarm intelligence‐based optimal device deployment in heterogeneous Internet of Things networks for wind farm application

Defects Prediction Method for Radiographic Images Based on Random PSO Using Regional Fluctuation Sensitivity

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