Row‐level algorithm to improve real‐time performance of glass tube defect detection in the production phase

Vitis, Gabriele Antonio De; Foglia, Pierfrancesco; Prete, Cosimo Antonio

doi:10.1049/iet-ipr.2019.1506

Cited by 18 publications

(2 citation statements)

References 27 publications

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“…We have applied proposed DSDRR to three algorithms: Canny [18], MAGDDA [57], and Niblack's [19] (Table 2). In case of Canny, a Gaussian filter is also applied to smooth the image and then the magnitude of gradient at each pixel location is calculated.…”

Section: Considered Algorithms and Configurationsmentioning

confidence: 99%

A pre‐processing technique to decrease inspection time in glass tube production lines

Vitis

Foglia

Prete

2021

IET Image Processing

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In case of glass tube for pharmaceutical applications, high-quality defect detection is made via inspection systems based on computer vision. The processing must guarantee realtime inspection and meet increasing rate and quality requirements. Defect detection in glass tubes is complicated by aspects that hamper the efficiency of state-of-the-art techniques. This paper presents a pre-processing algorithm which excludes portions of the image where defects are surely absent. The approach decreases the time for defect detection and classification phases (any detection algorithm can be applied), as they are applied only in high-probability candidate sub-image. We derive a methodology to get robust values of algorithm's parameters during production. The algorithm relies on detrended standard deviation and double threshold hysteresis, which solve issues related to the misalignment between illuminator and acquisition camera, and enable a robust detection despite rotation, vibration, and irregularities of tubes. We consider Canny, MAGDDA, and Niblack algorithms. The solution keeps the detection quality of such algorithms and reaches a 4.69× throughput gain. It represents a methodology to obtain defect detection in timeconstrained environments through a software-only approach, and can be exploited in parallel/accelerated solutions and in contexts where a linear camera is utilized on both flat and uneven surfaces.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Considered Algorithms and Configurationsmentioning

confidence: 99%

A pre‐processing technique to decrease inspection time in glass tube production lines

Vitis

Foglia

Prete

2021

IET Image Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…I N the industrial production process, defect detection of products is an indispensable part [1], [2]. Influenced by the production environment, equipment and other factors, various defects appear on the surface of metal products, which are commonly shown in Figure 1.…”

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confidence: 99%

Trident-LK Net: A Lightweight Trident Structure Network With Large Kernel for Muti-Scale Defect Detection

Yang,

Wang,

Qian

et al. 2023

IEEE Access

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Identifying defects at different scales is a challenge in industrial defect detection. To solve this problem, many multi-scale feature fusion networks have been proposed to improve multi-scale target detection accuracy by fusing fine-grained information from shallow networks and semantic information from deep networks. This approach requires the introduction of extra parameters. Thinking from another perspective, can the accuracy of multi-scale target detection be improved by fusing the feature information under different receptive fields? For this purpose, we designed a three-layer network structure called Trident-LK Net. our model uses convolutional kernels of different sizes (31, 25, 1) in the feature extraction phase and establishes cross-fusion connections. This omits the feature fusion part and greatly reduces the network parameters while obtaining a good detection accuracy. Finally we perform experiments on the neu-det dataset and the gc10 dataset to verify the feasibility of our idea. While keeping the number of parameters to a minimum, our model achieves competitive detection results on the neu-det dataset (76.9% mAP) and optimal on the gc10 dataset (63.55% mAP). INDEX TERMSConvolutional neural network, Defect detection, Feature fusion, Lightweight network I. INTRODUCTION FIGURE 1. six types of defects in NEU-DET dataset (a) Cr; (b) In; (c) Pa; (d) Ps; (e) Rs; (f) Sc.neural networks has brought new solution ideas to defect

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Using Emotion Recognition and Temporary Mobile Social Network in On-Board Services for Car Passengers

Cimino

Tecco

Foglia

et al. 2023

Communications in Computer and Information Science

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Row‐level algorithm to improve real‐time performance of glass tube defect detection in the production phase

Cited by 18 publications

References 27 publications

A pre‐processing technique to decrease inspection time in glass tube production lines

A pre‐processing technique to decrease inspection time in glass tube production lines

Trident-LK Net: A Lightweight Trident Structure Network With Large Kernel for Muti-Scale Defect Detection

Using Emotion Recognition and Temporary Mobile Social Network in On-Board Services for Car Passengers

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