scholarly journals Double Low-Rank and Sparse Decomposition for Surface Defect Segmentation of Steel Sheet

2018 ◽  
Vol 8 (9) ◽  
pp. 1628 ◽  
Author(s):  
Shiyang Zhou ◽  
Shiqian Wu ◽  
Huaiguang Liu ◽  
Yang Lu ◽  
Nianzong Hu
Keyword(s):  
Steel Sheet ◽  
Surface Defect ◽  
Detection System ◽  
Saliency Detection ◽  
Image Understanding ◽  
Low Rank ◽  
Sparse Decomposition ◽  
Detection Model ◽  
Otsu’S Method ◽  
Surface Defect Detection

Surface defect segmentation supports real-time surface defect detection system of steel sheet by reducing redundant information and highlighting the critical defect regions for high-level image understanding. Existing defect segmentation methods usually lack adaptiveness to different shape, size and scale of the defect object. Based on the observation that the defective area can be regarded as the salient part of image, a saliency detection model using double low-rank and sparse decomposition (DLRSD) is proposed for surface defect segmentation. The proposed method adopts a low-rank assumption which characterizes the defective sub-regions and defect-free background sub-regions respectively. In addition, DLRSD model uses sparse constrains for background sub-regions so as to improve the robustness to noise and uneven illumination simultaneously. Then the Laplacian regularization among spatially adjacent sub-regions is incorporated into the DLRSD model in order to uniformly highlight the defect object. Our proposed DLRSD-based segmentation method consists of three steps: firstly, using DLRSD model to obtain the defect foreground image; then, enhancing the foreground image to establish the good foundation for segmentation; finally, the Otsu’s method is used to choose an optimal threshold automatically for segmentation. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in terms of both subjective and objective tests. Meanwhile, the proposed method is applicable to industrial detection with limited computational resources.

scholarly journals A Deep Multiscale Fusion Method via Low-Rank Sparse Decomposition for Object Saliency Detection Based on Urban Data in Optical Remote Sensing Images

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Cheng Zhang ◽  
Dan He
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Saliency Detection ◽  
Saliency Map ◽  
Low Rank ◽  
Optical Remote Sensing ◽  
Fusion Method ◽  
Remote Sensing Images ◽  
Sparse Decomposition ◽  
Depth Feature

The urban data provides a wealth of information that can support the life and work for people. In this work, we research the object saliency detection in optical remote sensing images, which is conducive to the interpretation of urban scenes. Saliency detection selects the regions with important information in the remote sensing images, which severely imitates the human visual system. It plays a powerful role in other image processing. It has successfully made great achievements in change detection, object tracking, temperature reversal, and other tasks. The traditional method has some disadvantages such as poor robustness and high computational complexity. Therefore, this paper proposes a deep multiscale fusion method via low-rank sparse decomposition for object saliency detection in optical remote sensing images. First, we execute multiscale segmentation for remote sensing images. Then, we calculate the saliency value, and the proposal region is generated. The superpixel blocks of the remaining proposal regions of the segmentation map are input into the convolutional neural network. By extracting the depth feature, the saliency value is calculated and the proposal regions are updated. The feature transformation matrix is obtained based on the gradient descent method, and the high-level semantic prior knowledge is obtained by using the convolutional neural network. The process is iterated continuously to obtain the saliency map at each scale. The low-rank sparse decomposition of the transformed matrix is carried out by robust principal component analysis. Finally, the weight cellular automata method is utilized to fuse the multiscale saliency graphs and the saliency map calculated according to the sparse noise obtained by decomposition. Meanwhile, the object priors knowledge can filter most of the background information, reduce unnecessary depth feature extraction, and meaningfully improve the saliency detection rate. The experiment results show that the proposed method can effectively improve the detection effect compared to other deep learning methods.

Research on Key Technology of Cold-Rolled Aluminum Plate Surface Defect Detection System

2013 ◽  
Vol 433-435 ◽  
pp. 915-918 ◽  
Author(s):  
Hai Lun Zhang ◽  
Xing Guang Qi ◽  
Xiao Ting Li
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Detection System ◽  
Aluminum Plate ◽  
Aluminum Surface ◽  
Image Processing Algorithm ◽  
Key Technologies ◽  
Rolled Aluminum ◽  
Surface Defect Detection ◽  
Cold Rolled

This paper presents the research of several key technologies during the implementation of cold-rolling aluminum surface defect detection system, including the difficulty of achieving these key technologies and the improvement of image processing algorithm. Through the installation and commissioning on actual production line, summarize and analyze the requirements of the hardware and software design for highly reflective aluminum plate, to achieve the control of product quality at present.

scholarly journals Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD

2018 ◽  
Vol 8 (9) ◽  
pp. 1678 ◽  
Author(s):  
Yiting Li ◽  
Haisong Huang ◽  
Qingsheng Xie ◽  
Liguo Yao ◽  
Qipeng Chen
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Method ◽  
Detection Algorithm ◽  
Single Shot ◽  
Detection Model ◽  
Machine Learning Methods ◽  
Network Methods ◽  
Surface Defect Detection

This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) MobileNet into the MobileNet-SSD. Then, a detection method for surface defects was proposed based on the MobileNet-SSD. Specifically, the structure of the SSD was optimized without sacrificing its accuracy, and the network structure and parameters were adjusted to streamline the detection model. The proposed method was applied to the detection of typical defects like breaches, dents, burrs and abrasions on the sealing surface of a container in the filling line. The results show that our method can automatically detect surface defects more accurately and rapidly than lightweight network methods and traditional machine learning methods. The research results shed new light on defect detection in actual industrial scenarios.

scholarly journals Deep Active Learning for Surface Defect Detection

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1650 ◽  
Author(s):  
Xiaoming Lv ◽  
Fajie Duan ◽  
Jia-Jia Jiang ◽  
Xiao Fu ◽  
Lin Gan
Keyword(s):  
Active Learning ◽  
Object Detection ◽  
Defect Detection ◽  
Surface Defect ◽  
Candidate List ◽  
Detection Model ◽  
Detection Systems ◽  
Learning Framework ◽  
Surface Defect Detection ◽  
Sampling Scale

Most of the current object detection approaches deliver competitive results with an assumption that a large number of labeled data are generally available and can be fed into a deep network at once. However, due to expensive labeling efforts, it is difficult to deploy the object detection systems into more complex and challenging real-world environments, especially for defect detection in real industries. In order to reduce the labeling efforts, this study proposes an active learning framework for defect detection. First, an Uncertainty Sampling is proposed to produce the candidate list for annotation. Uncertain images can provide more informative knowledge for the learning process. Then, an Average Margin method is designed to set the sampling scale for each defect category. In addition, an iterative pattern of training and selection is adopted to train an effective detection model. Extensive experiments demonstrate that the proposed method can render the required performance with fewer labeled data.

The PCB Surface Defect Detection System Based on GPU Acceleration

2015 ◽  
Vol 713-715 ◽  
pp. 347-352
Author(s):  
Yu Liu ◽  
Si Zhe Li
Keyword(s):  
Defect Detection ◽  
Rapid Detection ◽  
Surface Defect ◽  
Printed Circuit Board ◽  
Detection System ◽  
Processing System ◽  
Circuit Board ◽  
Computing Platform ◽  
Surface Defect Detection ◽  
Time Requirements

Defective products are unavoidable in printed circuit board production process, so rapid detection and identificationmethods are badly in need of. PCB surface defect detection including a series of processing such as surface imagecapture, mixed noise filtering,images registering and so on, so it takes a lot of CPU time. To improve detection speed, based on GPU parallel computing platform, we designed a reasonable parallel processing system for PCB defect detectionto meet the need of real-time requirements of a production line. Experimental results show that parallel image processing algorithms based on GPU can achieve good results compared to the CPU-based serial algorithm (with speed up ratio up to8.34 in this paper),providing a new approachfor rapid detection of PCB surface defect.

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