scholarly journals A Deep-Learning-based 3D Defect Quantitative Inspection System in CC Products Surface

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 980 ◽  
Author(s):  
Liming Zhao ◽  
Fangfang Li ◽  
Yi Zhang ◽  
Xiaodong Xu ◽  
Hong Xiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Surface Region ◽  
Inspection System ◽  
Scanning System ◽  
Inspection Method ◽  
Scanning Method ◽  
Image Scanning ◽  
Defects Inspection ◽  
Inspection Strategy ◽  
Defects Classification

To create an intelligent surface region of interests (ROI) 3D quantitative inspection strategy a reality in the continuous casting (CC) production line, an improved 3D laser image scanning system (3D-LDS) was established based on binocular imaging and deep-learning techniques. In 3D-LDS, firstly, to meet the requirements of the industrial application, the CCD laser image scanning method was optimized in high-temperature experiments and secondly, we proposed a novel region proposal method based on 3D ROI initial depth location for effectively suppressing redundant candidate bounding boxes generated by pseudo-defects in a real-time inspection process. Thirdly, a novel two-step defects inspection strategy was presented by devising a fusion deep CNN model which combined fully connected networks (for defects classification/recognition) and fully convolutional networks (for defects delineation). The 3D-LDS’ dichotomous inspection method of defects classification and delineation processes are helpful in understanding and addressing challenges for defects inspection in CC product surfaces. The applicability of the presented methods is mainly tied to the surface quality inspection for slab, strip and billet products.

Robust Fabric Defects Inspection System Using Deep Learning Architecture

2021 ◽  
Vol 50 (1) ◽  
pp. 20200778
Author(s):  
T. Shanthi ◽  
M. E. Paramasivam ◽  
C. Prakash ◽  
K. Manju ◽  
Eldho Paul ◽  
...  
Keyword(s):  
Deep Learning ◽  
Inspection System ◽  
Defects Inspection

scholarly journals Product Inspection Methodology via Deep Learning: An Overview

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5039
Author(s):  
Tae-Hyun Kim ◽  
Hye-Rin Kim ◽  
Yeong-Jun Cho
Keyword(s):  
Deep Learning ◽  
Product Quality ◽  
Inspection System ◽  
Quality Inspection ◽  
Learning Models ◽  
Unified Framework ◽  
Learning Techniques ◽  
Inspection Systems ◽  
System Maintenance ◽  
Test Scenarios

In this study, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. In addition, we explain the steps for building a deep-learning-based inspection system in detail. Second, we address connection schemes that efficiently link deep learning models to product inspection systems. Finally, we propose an effective method that can maintain and enhance a product inspection system according to improvement goals of the existing product inspection systems. The proposed system is observed to possess good system maintenance and stability owing to the proposed methods. All the proposed methods are integrated into a unified framework and we provide detailed explanations of each proposed method. In order to verify the effectiveness of the proposed system, we compare and analyze the performance of the methods in various test scenarios. We expect that our study will provide useful guidelines to readers who desire to implement deep-learning-based systems for product inspection.

Image-Based Comprehensive Maintenance and Inspection Method for Bridges Using Deep Learning

2018 ◽  
Author(s):  
Xuefeng Zhao ◽  
Shengyuan Li ◽  
Hongguo Su ◽  
Lei Zhou ◽  
Kenneth J. Loh
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Visual Inspection ◽  
Health State ◽  
Bridge Management ◽  
Inspection Method ◽  
Maintenance Work ◽  
Maintenance And Inspection ◽  
Fine Turning

Bridge management and maintenance work is an important part for the assessment the health state of bridge. The conventional management and maintenance work mainly relied on experienced engineering staffs by visual inspection and filling in survey forms. However, the human-based visual inspection is a difficult and time-consuming task and its detection results significantly rely on subjective judgement of human inspectors. To address the drawbacks of human-based visual inspection method, this paper proposes an image-based comprehensive maintenance and inspection method for bridges using deep learning. To classify the types of bridges, a convolutional neural network (CNN) classifier established by fine-turning the AlexNet is trained, validated and tested using 3832 images with three types of bridges (arch, suspension and cable-stayed bridge). For the recognition of bridge components (tower and deck of bridges), a Faster Region-based Convolutional Neural Network (Faster R-CNN) based on modified ZF-net is trained, validated and tested by utilizing 600 bridge images. To implement the strategy of a sliding window technique for the crack detection, another CNN from fine-turning the GoogLeNet is trained, validated and tested by employing a databank with cropping 1455 raw concrete images into 60000 intact and cracked images. The performance of the trained CNNs and Faster R-CNN is tested on some new images which are not used for training and validation processes. The test results substantiate the proposed method can indeed recognize the types and components and detect cracks for a bridges.

scholarly journals Deep Learning Framework for Steel Surface Defects Classification

2019 ◽  
Vol 4 (1) ◽  
pp. 25-32
Author(s):  
Ranganath Singari ◽  
Karun Singla ◽  
Gangesh Chawla
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Architecture ◽  
Steel Surface ◽  
Surface Defects ◽  
Fixed Number ◽  
Quality Level ◽  
Acceptance Sampling ◽  
Quality Inspection ◽  
Defects Classification

Deep learning has offered new avenues in the field of industrial management. Traditional methods of quality inspection such as Acceptance Sampling relies on a probabilistic measure derived from inspecting a sample of finished products. Evaluating a fixed number of products to derive the quality level for the complete batch is not a robust approach. Visual inspection solutions based on deep learning can be employed in the large manufacturing units to improve the quality inspection units for steel surface defect detection. This leads to optimization of the human capital due to reduction in manual intervention and turnaround time in the overall supply chain of the industry. Consequently, the sample size in the Acceptance sampling can be increased with minimal effort vis-à-vis an increase in the overall accuracy of the inspection. The learning curve of this work is supported by Convolutional Neural Network which has been used to extract feature representations from grayscale images to classify theinputs into six types of surface defects. The neural network architecture is compiled in Keras framework using Tensorflow backend with state of the art Adam RMS Prop with Nesterov Momentum (NADAM) optimizer. The proposed classification algorithm holds the potential to identify the dominant flaws in the manufacturing system responsible for leaking costs.

Pipe morphology defects inspection system with active stereo omnidirectional vision sensor

2016 ◽  
Vol 45 (11) ◽  
pp. 1117005
Author(s):  
汤一平 Tang Yiping ◽  
鲁少辉 Lu Shaohui ◽  
吴 挺 Wu Ting ◽  
韩国栋 Han Guodong
Keyword(s):  
Inspection System ◽  
Omnidirectional Vision ◽  
Vision Sensor ◽  
Defects Inspection ◽  
Active Stereo

Automatic Ultrasonic Inspection Method of Railway Axle and its Remaining Life Prediction

2011 ◽  
Vol 213 ◽  
pp. 291-296 ◽  
Author(s):  
Hong Wei Hu ◽  
Xiong Bing Li ◽  
Xiang Hong Wang ◽  
Yi Min Shao
Keyword(s):  
High Speed ◽  
Fatigue Cracks ◽  
Ultrasonic Inspection ◽  
Inspection System ◽  
Remaining Life ◽  
High Speed Railway ◽  
Inspection Method ◽  
Spiral Trajectory ◽  
Image Element ◽  
The Matrix

With the high speed railway utilization, the probability of defects or fatigue cracks in railway axles is increased. An automatic ultrasonic inspection system for railway axles is presented. This system uses combined probes and inspects the defects with spiral trajectory along the axis of the axle. Through the matrix representation of C-scan image element, a defect edge extraction method is adopted, with which the defect parameters of crack are obtained automatically. Based on these defect parameters, the stress intensity factor is assessed by svm regression and the method to predict remaining life is proposed.

scholarly journals Computer vision–based automatic rod-insulator defect detection in high-speed railway catenary system

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141877394 ◽  
Author(s):  
Ye Han ◽  
Zhigang Liu ◽  
DJ Lee ◽  
Wenqiang Liu ◽  
Junwen Chen ◽  
...  
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
High Speed ◽  
Power Transmission ◽  
Estimation Algorithm ◽  
Inspection System ◽  
High Speed Railway ◽  
Inspection Method ◽  
Railway Systems ◽  
Catenary System

Maintenance of catenary system is a crucial task for the safe operation of high-speed railway systems. Catenary system malfunction could interrupt railway service and threaten public safety. This article presents a computer vision algorithm that is developed to automatically detect the defective rod-insulators in a catenary system to ensure reliable power transmission. Two key challenges in building such a robust inspection system are addressed in this work, the detection of the insulators in the catenary image and the detection of possible defects. A two-step insulator detection method is implemented to detect insulators with different inclination angles in the image. The sub-images containing cantilevers and rods are first extracted from the catenary image. Then, the insulators are detected in the sub-image using deformable part models. A local intensity period estimation algorithm is designed specifically for insulator defect detection. Experimental results show that the proposed method is able to automatically and reliably detect insulator defects including the breakage of the ceramic discs and the foreign objects clamped between two ceramic discs. The performance of this visual inspection method meets the strict requirements for catenary system maintenance.

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