scholarly journals An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 644 ◽  
Author(s):  
Qinbang Zhou ◽  
Renwen Chen ◽  
Bin Huang ◽  
Chuan Liu ◽  
Jie Yu ◽  
...  
Keyword(s):  
Automobile Industry ◽  
Surface Defects ◽  
Hessian Matrix ◽  
Human Vision ◽  
Vehicle Body ◽  
Support Vector ◽  
Inspection System ◽  
Light Sources ◽  
Automatic Inspection ◽  
Inspection Process

Automobile surface defects like scratches or dents occur during the process of manufacturing and cross-border transportation. This will affect consumers’ first impression and the service life of the car itself. In most worldwide automobile industries, the inspection process is mainly performed by human vision, which is unstable and insufficient. The combination of artificial intelligence and the automobile industry shows promise nowadays. However, it is a challenge to inspect such defects in a computer system because of imbalanced illumination, specular highlight reflection, various reflection modes and limited defect features. This paper presents the design and implementation of a novel automatic inspection system (AIS) for automobile surface defects which are the located in or close to style lines, edges and handles. The system consists of image acquisition and image processing devices, operating in a closed environment and noncontact way with four LED light sources. Specifically, we use five plane-array Charge Coupled Device (CCD) cameras to collect images of the five sides of the automobile synchronously. Then the AIS extracts candidate defect regions from the vehicle body image by a multi-scale Hessian matrix fusion method. Finally, candidate defect regions are classified into pseudo-defects, dents and scratches by feature extraction (shape, size, statistics and divergence features) and a support vector machine algorithm. Experimental results demonstrate that automatic inspection system can effectively reduce false detection of pseudo-defects produced by image noise and achieve accuracies of 95.6% in dent defects and 97.1% in scratch defects, which is suitable for customs inspection of imported vehicles.

Surface Defect Inspection and Classification of Segment Magnet by Using Machine Vision Technique

2011 ◽  
Vol 339 ◽  
pp. 32-35 ◽  
Author(s):  
Hong Hai Jiang ◽  
Guo Fu Yin
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Surface Defects ◽  
Processing System ◽  
Support Vector ◽  
Inspection System ◽  
Low Contrast ◽  
Constituent Material ◽  
Canny Edge

In this paper, we propose a machine vision based approach for detecting and classifying irregular low-contrast surface defects of segment magnet. The constituent material of it is ferrite which varies from silver gray to black in color .For this reason, the defects embedded in a low-contrast surface show no big different from its surrounding region, and even worse, all the surfaces and chamfers of segment magnet must be inspected. Our system is able to analyze all surfaces under inspection, to discover and classify its defects by means of image processing algorithms and support vector machine (SVM). A working prototype of the system has been built and tested to validate the proposed approach and to reproduce the difficult issues of the inspection system. The developed prototype includes three subsystems: an array of several CCD area cameras (Fig.1); a controllable roller LED light source(Fig.1); and a PC-based image processing system. The detection of the defects is performed by means of Canny edge detection, morphology and other feature extraction operations. The image processing and classification results demonstrate that the proposed method can identify surface defects effectively.

scholarly journals Pengembangan Sistem Penilaian Kematangan Tandan Buah Segar Kelapa Sawit menggunakan Citra 680 dan 750 NM

2020 ◽  
Vol 7 (2) ◽  
pp. 379
Author(s):  
Agung Wahyu Setiawan ◽  
Alfie R. Ananda
Keyword(s):  
Support Vector Machine ◽  
Test Data ◽  
Palm Oil ◽  
Image Contrast ◽  
Human Vision ◽  
Training Data ◽  
Training Dataset ◽  
Support Vector ◽  
Light Sources ◽  
Maturity Level

<p class="Abstrak">Salah satu permasalahan utama dalam industri kelapa sawit adalah proses sortasi Tandan Buah Segar (TBS) di pabrik kelapa sawit. Parameter yang digunakan dalam sortasi TBS adalah jumlah brondolan kelapa sawit. Pada saat ini, sortasi dilakukan oleh <em>grader</em> yang bersifat subyektif dan sering kali tidak konsisten. Hal ini terjadi karena keterbatasan penglihatan dan kemampuan manusia untuk mengolah informasi jumlah brondolan setiap TBS dalam waktu yang terbatas. Oleh karena itu, pada penelitian ini dikembangkan sistem penilaian kematangan TBS kelapa sawit berbasis spektroskopi dan nilai kontras citras. Sumber cahaya yang digunakan pada penelitian ini adalah lampu berjenis <em>Light-emitting Diode</em> (LED) dengan panjang gelombang 680 dan 750 nm. Akuisisi citra TBS dilakukan dengan menggunakan kamera DSLR yang telah dimodifikasi. sehingga diperoleh dua citra TBS pada panjang gelombang 680 dan 750 nm. Kemudian, dilakukan perhitungan nilai kontras kedua citra tersebut. Dalam penelitian ini, terdapat 24 TBS yang digunakan sebagai data latih, dengan komposisi 10 TBS matang dan 14 TBS mentah. Data uji yang digunakan berjumlah 77 TBS yang terdiri dari 38 matang dan 39 mentah. Pada penelitian ini, <em>Support Vector Machine</em> (SVM) digunakan sebagai metode klasifikasi. Akurasi data latih yang diperoleh adalah 66,67%. Sedangkan akurasi data uji dari sistem yang dikembangkan dalam penelitian ini adalah 57,14%. Hasil yang diperoleh ini masih perlu diperbaiki untuk meningkatkan akurasi sistem dengan cara menambah jumlah data, baik data latih maupun uji, serta menggunakan pembelajaran mesin.</p><p class="Abstrak"> </p><p class="Abstrak"><strong><em>Abstract</em></strong></p><p class="Abstrak"><em>One of the main problems in the palm oil industry is the grading of Fresh Fruit Bunches (FFB) in the palm oil mills. The parameter used for the process is the number of fruitlets detached from the bunch. Nowadays, the FFB grading is conducted by graders which is subjective and often inconsistent due to the limitation of human vision and ability to process information on the number of fruitlets detached per FFB in a very limited time. Therefore, this study developed a grading system to assess and estimate the FFB maturity based on spectroscopy and image contrast value. From the literature review, visible light and NIR spectrum in 680 and 780 nm can be used as light sources to detect the maturity level of FFB. DSLR camera is used to acquire the FFB image. Using this scheme, two FFB images in 680 and 750 nm are obtained. The next process is to calculate the image contrast. In this research, there are 24 FFB that are used as training data that consists of 10 ripe and 14 unripe. A total of 77 FFB are used as test data that consists of 38 ripe and 39 unripe. Support Vector Machine (SVM) is used in this research to classify the maturity level of FFB. The accuracy of the training dataset is 66.67%. Meanwhile, the accuracy of the test data is 57.14%. Future works will focus on enhancing accuracy of the system through increasing the number of training and testing data using machine learning.</em></p>

A vision-based character inspection system for tire mold

2017 ◽  
Vol 37 (2) ◽  
pp. 230-237 ◽  
Author(s):  
Nian Cai ◽  
Yuchao Chen ◽  
Gen Liu ◽  
Guandong Cen ◽  
Han Wang ◽  
...  
Keyword(s):  
Image Processing ◽  
Support Vector ◽  
Inspection System ◽  
Image Mosaic ◽  
Automatic Inspection ◽  
Processing Methods ◽  
Inspection Method ◽  
Image Preprocessing ◽  
Content Type ◽  
Inspection Task

Purpose This paper aims to design an automatic inspection system for the characters on tire molds, which involves a vision-based inspection method for the characters on tire molds. Design/methodology/approach An automatic inspection equipment is designed according to the features of the tire mold. To implement the inspection task, the corresponding image processing methods are designed, including image preprocessing, image mosaic, image locating and character inspection. Image preprocessing mainly contains fitting the contours of the acquired tire mold images and those of the computer aided design (CAD) as the arcs of two circles and polar transformation of the acquired images and the CAD. Then, the authors propose a novel framework to locate the acquired images into the corresponding mosaicked tire mold image. Finally, a character inspection scheme is proposed by combining an support-vector-machine-based character recognition method and a string matching approach. At the stages of image locating and character inspection, image mosaic is simultaneously used to label the defects in the mosaicked tire mold image, which is based on histograms-of-gradients features. Findings The experimental results indicate that the designed automatic inspection system can inspect the characters on the tire mold with a high accuracy at a reasonable time consumption. Practical implications The designed automatic inspection system can detect the carving faults for the characters on the tire molds, which are the cases that the characters are wrongly added, deleted or modified on the tire mold. Originality/value To the best of the authors’ knowledge, this is the first automatic vision-based inspection system for the characters on tire molds. An inspection equipment is designed and many novel image processing methods are proposed to implement the inspection task. The designed system can be widely applied in the industry.

Automatic Inspection System for Surface Defect of Precision Steel Ball

2014 ◽  
Vol 685 ◽  
pp. 405-409
Author(s):  
Yi Ji Chen ◽  
Jhy Cherng Tsai ◽  
Ya Chen Hsu
Keyword(s):  
Surface Defect ◽  
Surface Defects ◽  
Optical Measurement ◽  
Steel Ball ◽  
Inspection System ◽  
Automatic Inspection ◽  
Robust Method ◽  
Defect Inspection ◽  
Critical Components ◽  
Steel Balls

Precision steel ball is one of the most critical components for rolling transmission. As precision ball affects the performance of precision transmission system, fully inspection of these balls is an urgent need for the industry. This paper is to develop a real-time inspection system for surface defects of precision steel ball with fast and robust method and mechanism. The developed system consists of an optical measurement module as well as a mechanism module for full surface inspecting of the steel ball. The minimum defect and area can be detected by the developed system are 0.1mm and 0.01 mm2 respectively. The developed system has been testified against the designed specifications at speed higher than 3pc/sec and less than 0.5% missing rate. It verified the resolution, accuracy and robustness of the developed system which is capable for final defect inspection of steel balls for grade 100 bearing.

Optimizing the Lighting in Automatic Inspection System using Monte Carlo Method

1999 ◽  
Vol 38 (Part 1, No. 10) ◽  
pp. 6123-6129 ◽  
Author(s):  
Li Chen ◽  
Xinsheng Wang ◽  
Masafumi Suzuki ◽  
Noboru Yoshimura
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Inspection System ◽  
Automatic Inspection
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