A Robust Completed Local Binary Pattern (RCLBP) for Surface Defect Detection

Gyimah, Nana Kankam; Girma, Abenezer; Mahmoud, Mahmoud Nabil; Nateghi, Shamila; Homaifar, Abdollah; Opoku, Daniel

doi:10.1109/smc52423.2021.9659140

Cited by 20 publications

(16 citation statements)

References 29 publications

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“…This approach integrated non-local mean lters and CLBP for feature extraction. The results demonstrated excellent performance for the RCLBP methodology, with precision and recall values of 0.78 and 0.72, respectively (Gyimah et al, 2021). Additionally, the RCLBP structure effectively detected surface defects characterized by both intraclass and interclass feature variations and demonstrated moderate accuracy in additive Gaussian noise.…”

Section: Related Workmentioning

confidence: 85%

“…Nonetheless, the outcomes for pitted surface and inclusion defects were marginally lower than those of other categories. In a separate study, Gyimah et al (2021) proposed applying a Robust Completed Binary Pattern (RCLBP) framework for detecting and classifying defects. This approach integrated non-local mean lters and CLBP for feature extraction.…”

Section: Related Workmentioning

confidence: 99%

“…A curvelet transform is a multi-scale geometric analysis technique that offers distinct advantages, including anisotropic scaling and directionality. In the context of the proposed dataset, the curvelet transform proves valuable in detecting and analyzing longitudinal cracks (Gyimah et al, 2021). Its ability to capture and represent edges with curvilinear or linear shapes makes it particularly suitable for examining such features.…”

Section: Fourier Transformsmentioning

confidence: 99%

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Defect Detection and Classification on Hot-Rolled Steel Using Machine Learning and Deep Learning Algorithms: A Comparative Analysis

Otieno,

Kotak,

Parmar

2023

Preprint

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Elevated concerns regarding sustainable manufacturing have resulted in increased efforts to deploy data-driven methodologies incorporating automated systems for fault analysis. In particular, manufacturing is increasingly focused on creating systems that detect and categorize defects, facilitating root-cause investigations. This research paper delves into the use of machine learning (ML) and deep learning (DL) approaches for defect detection in hot-rolled steel, focusing on examining the robustness of the different defect detection techniques. In the case of ML approaches, three primary feature extraction techniques: local binary pattern (LBP), histogram of oriented gradients (HOG), and gray-level co-occurrence matrix (GLCM), were employed. Generally, GLCM demonstrated relatively good performance, i.e., attained precision, recall, and f1-score values of 0.91 with a support vector machine (SVM) classifier. Similarly, using SVM, LBP attains precision, recall, and f1-score values of 0.89. Deep-learning methodologies such as convolutional neural networks (CNN), CNN plus VGG19 (CNN + VGG19), and you only look once version 7 (YOLOv7) were employed to investigate and classify the hot-rolled steel defects. CNN + VGG-19 and YOLOv7 exhibited excellent defect detection performance, attaining accuracy values of 0.9639 and 0.915, respectively. Overall, the results demonstrated relatively higher performance can be derived from using deep learning approaches such as CNN + VGG19 compared to traditional machine learning approaches.

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Section: Related Workmentioning

confidence: 85%

Section: Related Workmentioning

confidence: 99%

Section: Fourier Transformsmentioning

confidence: 99%

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Defect Detection and Classification on Hot-Rolled Steel Using Machine Learning and Deep Learning Algorithms: A Comparative Analysis

Otieno,

Kotak,

Parmar

2023

Preprint

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“…Therefore, researchers of traditional methods pay more attention to the improvement of feature extractors. Nana et al [58] used a modified local binary method for feature extraction of defect images. Although the above two methods can achieve good results, their robustness is poor, and it is difficult to distinguish multiple similar defects.…”

Section: Related Workmentioning

confidence: 99%

An Efficient End-to-End Multitask Network Architecture for Defect Inspection

Zhang

Yang

et al. 2022

Sensors

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Recently, computer vision-based methods have been successfully applied in many industrial fields. Nevertheless, automated detection of steel surface defects remains a challenge due to the complexity of surface defects. To solve this problem, many models have been proposed, but these models are not good enough to detect all defects. After analyzing the previous research, we believe that the single-task network cannot fully meet the actual detection needs owing to its own characteristics. To address this problem, an end-to-end multi-task network has been proposed. It consists of one encoder and two decoders. The encoder is used for feature extraction, and the two decoders are used for object detection and semantic segmentation, respectively. In an effort to deal with the challenge of changing defect scales, we propose the Depthwise Separable Atrous Spatial Pyramid Pooling module. This module can obtain dense multi-scale features at a very low computational cost. After that, Residually Connected Depthwise Separable Atrous Convolutional Blocks are used to extract spatial information under low computation for better segmentation prediction. Furthermore, we investigate the impact of training strategies on network performance. The performance of the network can be optimized by adopting the strategy of training the segmentation task first and using the deep supervision training method. At length, the advantages of object detection and semantic segmentation are tactfully combined. Our model achieves mIOU 79.37% and mAP@0.5 78.38% on the NEU dataset. Comparative experiments demonstrate that this method has apparent advantages over other models. Meanwhile, the speed of detection amount to 85.6 FPS on a single GPU, which is acceptable in the practical detection process.

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“…To address this problem, researchers have explored the use of heuristics and machine learning techniques to classify driver distraction based on data collected from sensors and cameras inside vehicles with the later approach been recently used. The machine learning approaches that have been employed in past studies include, but are not limited to, support vector machines (SVMs), decision trees, random forests, and 2D convolutional neural networks (CNN) (Aboah et al, 2023; Chilukuri et al, 2022; Gyimah et al, 2021; Gyimah et al, 2023; Keshinro, 2022).…”

Section: 0 Introductionmentioning

confidence: 99%

Classification of Human Driver Distraction Using 3D Convolutional Neural Networks

Kwakye,

Aboah,

Seong

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Distracted driving is a dangerous driving behavior that causes numerous accidents on US roads each year. It is critical to identify distracted drivers in order to prevent such accidents. Previous studies attempted to detect distracted driving using heuristics and machine learning; however, none of these methods could capture the problem's spatiotemporal features. As a result, the purpose of this study was to use a 3D convolutional neural network (CNN) that can capture both spatial and temporal information to classify distracted drivers based on facial features and behavioral cues. We used the Database to Enable Facial Analysis for Driving Studies (DEFADS), an open-source dataset containing 77 human subjects performing scripted driving-related activities, to achieve this goal. The PyTorch video library was used to train the model. The 3D CNN achieved an overall recall and precision of 97.6 and 98.1, respectively, indicating its efficacy in detecting distracted drivers in the real world.

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A Robust Completed Local Binary Pattern (RCLBP) for Surface Defect Detection

Cited by 20 publications

References 29 publications

Defect Detection and Classification on Hot-Rolled Steel Using Machine Learning and Deep Learning Algorithms: A Comparative Analysis

Defect Detection and Classification on Hot-Rolled Steel Using Machine Learning and Deep Learning Algorithms: A Comparative Analysis

An Efficient End-to-End Multitask Network Architecture for Defect Inspection

Classification of Human Driver Distraction Using 3D Convolutional Neural Networks

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