A cost-effective and automatic surface defect inspection system for hot-rolled flat steel

Luo, Qiwu; He, Yigang

doi:10.1016/j.rcim.2015.09.008

Cited by 96 publications

(58 citation statements)

References 25 publications

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“…From TABLE V, the noise robustness of ICNLP_S/C ranks first, followed by the ICLBP_S/C. When ηPSNR is no less than 0.85, the TPRs of our sub-schemes are (16,4) /C are slightly higher than those of ICLBP_S hriu2 (8,1)+(8,3)+ (16,2) /C, the feature dimension is nearly doubled. The parameter settings of (P,R) = (8,3) and (P,R) = (8,1)+(8,3)+(16,2) are thus recommended for single-resolution and multiresolution configuration, respectively.…”

Section: ) Results and Analysismentioning

confidence: 91%

“…However, due to the 3-D joint scheme of histograms, the feature dimensions increase to some extent, so the runtime overheads increase accordingly. As for our AOI instrument developed in [4], the average permitted classification time per image sample (256×256 pixel) can be estimated as…”

Section: ) Time-efficiency Evaluationmentioning

confidence: 99%

“…Luo et al developed a cost-effective hot-rolled steel AOI instrument. It was driven by hardware acceleration technique and the upper limit of rolling speed was elevated to 20 m/s [4]. Liu et al constructed an unsupervised Haar-Weibull-variance (HWV) model, and a higher TPR of 96.2% was achieved [7].…”

mentioning

confidence: 99%

“…2) Unstable gray values. The gray value is sensitive to cooling water [4], uneven illumination [5], multiple material types [8] and mechanical vibration [3], [9]. As shown in Fig.…”

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confidence: 99%

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Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

Luo

Sun

et al. 2019

IEEE Trans. Instrum. Meas.

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129

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Abstract-Efficient defect classification is one of the most important preconditions to achieve online quality inspection for hot-rolled strip steels. It is extremely challenging owing to various defect appearances, large intra-class variation, ambiguous inter-class distance, and unstable gray values. In this paper, a generalized completed local binary patterns (GCLBP) framework is proposed. Two variants of improved completed local binary patterns (ICLBP) and improved completed noise-invariant local-structure patterns (ICNLP) under the GCLBP framework are developed for steel surface defect classification. Different from conventional LBP variants, descriptive information hidden in nonuniform patterns is innovatively excavated for better defect representation. This work focuses on the following aspects: First, a lightweight searching algorithm is established for exploiting the dominant nonuniform patterns (DNUPs). Second, a hybrid pattern code mapping mechanism is proposed to encode all the uniform patterns and DNUPs. Third, feature extraction is carried out under the GCLBP framework. Finally, histogram matching is efficiently accomplished by simple nearest neighbor classifier (NNC). The classification accuracy and time-efficiency are verified on a widely recognized texture database (Outex) and a real-world steel surface defect database (NEU). The experimental results promise that the proposed method can be widely applied in online AOI instruments for hot-rolled strip steel.Index Terms-Surface defects, image classification, hot-rolled strips, local binary patterns (LBP), automatic optical inspection (AOI) instrument.

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Section: ) Results and Analysismentioning

confidence: 91%

Section: ) Time-efficiency Evaluationmentioning

confidence: 99%

mentioning

confidence: 99%

“…2) Unstable gray values. The gray value is sensitive to cooling water [4], uneven illumination [5], multiple material types [8] and mechanical vibration [3], [9]. As shown in Fig.…”

mentioning

confidence: 99%

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Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

Luo

Sun

et al. 2019

IEEE Trans. Instrum. Meas.

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129

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“…Similar to the testing procedure in [5], [11], we adopted five real workloads for objective evaluation. As shown in TABLE II, Financial1 is a write intensive trace file [27], Financial2 is a read intensive trace file [27], MSR is a common workload of large enterprise servers [28], Distilled represents typical usage patterns in a personal computer, and lastly [7], MillSSD is gathered from an industrial surface defect inspection device [29], with hardware configurations of Runcore RCS-V-T25 SSD, Intel X2 7400 and 2G DDR3. MillSSD is also a write intensive trace file due to its role for substantial image backup.…”

Section: ) Simulation Setupmentioning

confidence: 99%

Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories

Luo

Fang

Sun

et al. 2020

IEEE Trans. Circuits Syst. I

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Well understanding the access behavior of hot data is significant for NAND flash memory due to its crucial impact on the efficiency of garbage collection (GC) and wear leveling (WL), which respectively dominate the performance and life span of SSD. Generally, both GC and WL rely greatly on the recognition accuracy of hot data identification (HDI). However, in this paper, the first time we propose a novel concept of hot data prediction (HDP), where the conventional HDI becomes unnecessary. First, we develop a hybrid optimized echo state network (HOESN), where sufficiently unbiased and continuously shrunk output weights are learnt by a sparse regression based on L 2 and L 1/2 regularization. Second, quantum-behaved particle swarm optimization (QPSO) is employed to compute reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) for further improving prediction accuracy and reliability. Third, in the test on a chaotic benchmark (Rossler), the HOESN performs better than those of six recent state-of-the-art methods. Finally, simulation results about six typical metrics tested on five real disk workloads and on-chip experiment outcomes verified from an actual SSD prototype indicate that our HOESN-based HDP can reliably promote the access performance and endurance of NAND flash memories.Index Terms-NAND flash memory, solid state disk (SSD), echo state network (ESN), hot data prediction, regularization.

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Image Processing for Digital Twin

Pal

Mishra

Pal

et al. 2021

Springer Series in Advanced Manufacturing

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A cost-effective and automatic surface defect inspection system for hot-rolled flat steel

Cited by 96 publications

References 25 publications

Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories

Image Processing for Digital Twin

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