Surface Defect Detection of Rolled Steel Based on Lightweight Model

Zhou, Shujing; Yun, Zeng; Li, Sicheng; Zhu, Hao; Li, Xue; Zhang, Xin

doi:10.3390/app12178905

Cited by 11 publications

(8 citation statements)

References 28 publications

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“…The resulting outputs are then concatenated together along the channel dimension and finally fused with the input to form the output of RASPP. Convolution layers are used in three branches, and the size of the convolution kernel is (1,3,3). The input channel and the output channel are consistent, and the ReLU function [37] is used to activate the output.…”

Section: Residual Atrous Spatial Pyramid Pooling (Raspp)mentioning

confidence: 99%

“…The input channel and the output channel are consistent, and the ReLU function [37] is used to activate the output. The dilation rates of the convolutional layers are set to (1,3,6), respectively, to capture multi-scale feature information. The stride of all dilated convolutions is 1.…”

Section: Residual Atrous Spatial Pyramid Pooling (Raspp)mentioning

confidence: 99%

“…This method is quite costly and inefficient. Consequently, the automatic defect detection on the steel strip surface is essential to reduce costs and improve production efficiency [1]. The automatic defect detection methods based on computer vision have shown great advantages in the surface defect detection of steel strips [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Surface Defect Detection of Steel Strip with Double Pyramid Network

Zhou

et al. 2023

Applied Sciences

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Defect detection on the surface of the steel strip is essential for the quality assurance of the steel strip. Precise localization and classification, the two significant tasks of defect detection, still need to be completed due to the diversity of defect scales. In this paper, a residual atrous spatial pyramid pooling (RASPP) module is first designed to enrich the multi-scale information of the feature maps and increase the receptive field of the feature maps. Secondly, a double pyramid network (DPN) that combines RASPP and feature pyramid is proposed to fuse multi-scale features further so that similar semantic features are shared among the features of each layer. Finally, DPN-Detector, an automatic surface defects detection network, is proposed, which embeds the DPN module into Faster R-CNN and replaces the original detection head with a designed double head. Experiments are carried out on the steel strip surface defect dataset (NEU-DET), and the results show that the mAP of DPN-Detector is as high as 80.93%, which is 3.52% higher than that of the baseline network Faster R-CNN. The classification accuracy is 74.64%, and the detection speed reaches 18.62 FPS. The proposed method performs better robustness, classification and regression capability than other steel strip defect detection methods.

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Section: Residual Atrous Spatial Pyramid Pooling (Raspp)mentioning

confidence: 99%

Section: Residual Atrous Spatial Pyramid Pooling (Raspp)mentioning

confidence: 99%

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Surface Defect Detection of Steel Strip with Double Pyramid Network

Zhou

et al. 2023

Applied Sciences

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“…Its swift execution time renders it particularly suitable for real-time detection tasks. The architectural design of the YOLOv7 model is segmented into four distinct components: the input terminal, backbone network, neck, and detection head (Zhou et al, 2022). Within the input layer, samples undergo random preprocessing and are subsequently arranged in a mosaic pattern to augment their quality.…”

Section: Yolov7 (You Only Look Once)mentioning

confidence: 99%

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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“…If this type of accident cannot be prevented on time, the production equipment will be severely damaged and the production will be halted, and even personal injury will occur. (1)(2)(3)(4)(5) Thus far, many studies have put forward a series of solutions to the common problem of HSBS accidents. (6)(7)(8)(9)(10) Rusnák et al (6) proposed the method of increasing the final thickness of rolled steel to eliminate surface defects as much as possible.…”

Section: Introduction 11 Hot Steel-bar Stack Accidentsmentioning

confidence: 99%