Uneven Illumination Surface Defects Inspection Based on Saliency Detection and Intrinsic Image Decomposition

Qiu, Yuanhong; Tang, Lixin; Li, Bin; Niu, Suanlong; Niu, Tongzhi

doi:10.1109/access.2020.3032108

Cited by 12 publications

(8 citation statements)

References 51 publications

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“…The method proposed in this study can be applied to two processes: the process of assembling products in the assembly area when the parts are inputted as shown on the left of Figure 1 , and the process of judging the presence or absence of defects in the inspection area when products are inputted as shown on the right of Figure 1 . This inspection process determines the presence of defects related to the appearance or assembly state and not the function of the product [ 10 , 11 ]. The method proposed in this study performs alignment correction for round-shaped products as well, as shown in Figure 2 .…”

Section: Structure Of the Assembly And Inspection Process Systemmentioning

confidence: 99%

A Circular-Based Reference Point Extraction Method for Correcting the Alignment of Round Parts

Moon¹,

Kim

2022

Sensors

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For products such as smartphones, the technology gap between companies is gradually narrowing with the advancements in technology. Therefore, product design can be a visible strategy for differentiation. However, it is difficult to apply automated production and defect detection processes to the various designs that are being developed. This study proposes a high-speed circular measurement method for correcting the alignment of round parts, which is difficult in an automated process. For analyzing the performance of the proposed method, its processing speed and accuracy are compared with those of the existing methods. The results of the analysis indicate that the overall performance of the proposed method is better than those of the existing methods.

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Section: Structure Of the Assembly And Inspection Process Systemmentioning

confidence: 99%

A Circular-Based Reference Point Extraction Method for Correcting the Alignment of Round Parts

Moon¹,

Kim

2022

Sensors

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“…) || 1 b + (8) where, ( , ) f w b is the primal objective function and is the penalty factor that increases gradually along with iterations. Till now, we derive the unconstrained optimization objective function, which can facilitate the succeeding optimization algorithm design process.…”

Section: B Efficient Heuristic Alternating Optimization (Hao) Algorithmmentioning

confidence: 99%

“…Ever since the pioneer work of Itti et al [4], there has been an increasing interest in predicting this saliency map with computer algorithms [5]- [7]. Also, the advancement of saliency modeling technique has benefited a wide range of scientific and engineering fields, such as industrial defect detection [8], remote sensing interpretation [9], multi-media applications [10], etc.…”

Section: Introductionmentioning

confidence: 99%

Joint Learning of Discriminative Metric Space From Multi-Context Visual Scene for Unsupervised Salient Object Detection

Wang

2022

IEEE Access

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Mimicking the biological visual attention mechanism to discriminate visually appealing regions in natural scenes has been a hot research topic in recent years. However, designing computational models with self-driven capability for open world scenarios remains a challenging task which deserves to be further studied. In this paper, we propose an unsupervised learning approach to detect salient objects from images by fully exploiting the multi-context semantic information of the scenes. Specifically, a selfdriven model combing the idea of discriminative metric learning and structured sparse constraint is designed to find an optimal semantic mapping space for robust scene specific saliency prediction from complex environments. Meanwhile, a heuristic alternating optimization algorithm is developed to remove the ambiguity in the coarse geometric prior to generate a fine-grained discriminative model for saliency. On the basis of this, multi-context visual scenes are jointly modeled and fused to capture the image hierarchical structures for high-quality saliency map generation. Finally, we conduct experiments to verify the effectiveness of the proposed approach on four saliency benchmark datasets and compare it with 18 stateof-the-art saliency detection methods. Both qualitative saliency map and quantitative numerical index results indicate that our method has superior detection performance than the other counterparts under diversified scenes. Also, the proposed approach is applied to model wide synthetic aperture radar images for rapid target detection and promising results are obtained.INDEX TERMS Heuristic alternating optimization, salient object detection (SOD), SAR target detection, unsupervised learning.

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“…So, based on computer vision, surface defect detection remains challenging due to uneven illumination and non-stable image-obtaining conditions [19]. Low contrast and the heterogeneous patterns of defects make this task even more complicated.…”

Section: Introductionmentioning

confidence: 99%

Surface Illumination as a Factor Influencing the Efficacy of Defect Recognition on a Rolled Metal Surface Using a Deep Neural Network

Maruschak,

Konovalenko,

Osadtsa

et al. 2024

Applied Sciences

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Modern neural networks have made great strides in recognising objects in images and are widely used in defect detection. However, the output of a neural network strongly depends on both the training dataset and the conditions under which the image was acquired for analysis. We have developed a software–hardware method for evaluating the effect of variable lighting on the results of defect recognition using a neural network model. The proposed approach allows us to analyse the recognition results of an existing neural network model and identify the optimal range of illumination at which the desired defects are recognised most consistently. For this purpose, we analysed the variability in quantitative parameters (area and orientation) of damage obtained at different degrees of illumination for two different light sources: LED and conventional incandescent lamps. We calculated each image’s average illuminance and quantitative parameters of recognised defects. Each set of parameters represents the results of defect recognition for a particular illuminance level of a given light source. The proposed approach allows the results obtained using different light sources and illumination levels to be compared and the optimal source type/illuminance level to be figured out. This makes implementing a defect detection environment that allows the best recognition accuracy and the most controlled product quality possible. An analysis of a steel sheet surface showed that the best recognition result was achieved at an illuminance of ~200 lx. An illuminance of less than ~150 lx does not allow most defects to be recognised, whereas an illuminance larger than ~250 lx increases the number of small objects that are falsely recognised as defects.

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Uneven Illumination Surface Defects Inspection Based on Saliency Detection and Intrinsic Image Decomposition

Cited by 12 publications

References 51 publications

A Circular-Based Reference Point Extraction Method for Correcting the Alignment of Round Parts

A Circular-Based Reference Point Extraction Method for Correcting the Alignment of Round Parts

Joint Learning of Discriminative Metric Space From Multi-Context Visual Scene for Unsupervised Salient Object Detection

Surface Illumination as a Factor Influencing the Efficacy of Defect Recognition on a Rolled Metal Surface Using a Deep Neural Network

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