Defect Detection and Classification in Citrus Using Computer Vision

2009 ◽  
pp. 11-18 ◽  
Author(s):  
Jose J. Lopez ◽  
Emanuel Aguilera ◽  
Maximo Cobos
Keyword(s):  
Computer Vision ◽  
Defect Detection

PCB Defect Detection using Computer Vision based Symbolic Pattern Analysis Technique

2017 ◽  
Vol 7 (6) ◽  
pp. 413-420
Author(s):  
Vaddin Prathiba ◽  
◽  
M Nagendra ◽  
M Hanumantappa ◽  
◽  
...  
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
Pattern Analysis ◽  
Analysis Technique

Computer Vision Based Technique for Surface Defect Detection of Apples

2014 ◽  
pp. 111-121
Author(s):  
C. J. Prabhakar ◽  
S. H. Mohana
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
Surface Defects ◽  
Quality Standards ◽  
Production Costs ◽  
Automatic Inspection ◽  
Segmentation Technique ◽  
Surface Defect Detection ◽  
Fruit Grading

The automatic inspection of quality in fruits is becoming of paramount importance in order to decrease production costs and increase quality standards. Computer vision techniques are used in fruit industry for fruit grading, sorting, and defect detection. In this chapter, we review recent approaches for automatic inspection of quality in fruits using computer vision techniques. Particularly, we focus on the review of advances in computer vision techniques for automatic inspection of quality of apples based on surface defects. Finally, we present our approach to estimate the defects on the surface of an apple using grow-cut and multi-threshold based segmentation technique. The experimental results show that our method effectively estimates the defects on the surface of apples significantly more effectively than color based segmentation technique.

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.

Computer vision based system for apple surface defect detection

2002 ◽  
Vol 36 (2-3) ◽  
pp. 215-223 ◽  
Author(s):  
Qingzhong Li ◽  
Maohua Wang ◽  
Weikang Gu
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defect Detection

scholarly journals Automatic Defect Detection in Spring Clamp Production via Machine Vision

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xia Zhu ◽  
Renwen Chen ◽  
Yulin Zhang
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Machine Vision ◽  
Defect Detection ◽  
Detection System ◽  
Reliability And Validity ◽  
Laser Sensor ◽  
Defect Region ◽  
Gigabit Ethernet ◽  
Increasing Demand

There is an increasing demand for automatic online detection system and computer vision plays a prominent role in this growing field. In this paper, the automatic real-time detection system of the clamps based on machine vision is designed. It hardware is composed of a specific light source, a laser sensor, an industrial camera, a computer, and a rejecting mechanism. The camera starts to capture an image of the clamp once triggered by the laser sensor. The image is then sent to the computer for defective judgment and location through gigabit Ethernet (GigE), after which the result will be sent to rejecting mechanism through RS485 and the unqualified ones will be removed. Experiments on real-world images demonstrate that the pulse coupled neural network can extract the defect region and judge defect. It can recognize any defect greater than 10 pixels under the speed of 2.8 clamps per second. Segmentations of various clamp images are implemented with the proposed approach and the experimental results demonstrate its reliability and validity.

Research on Surface Defect Detection Technique of Rolling Element Based on Computer Vision

2014 ◽  
Vol 1006-1007 ◽  
pp. 773-778 ◽  
Author(s):  
Chuan Ren ◽  
Xiao Yu Xiu ◽  
Guo Hui Zhou
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
High Speed ◽  
Surface Defect ◽  
Surface Defects ◽  
Median Filter ◽  
Image Sensor ◽  
Threshold Method ◽  
Rolling Element ◽  
Surface Defect Detection

This paper proposed a new method of surface defect detection of rolling element based on computer vision, which adopted CCD digital camera as image sensor, and used digital image processing techniques to defect the surface defects of rolling element. The main steps include collect image, use an improved median filter to reduce the noise, increase or decrease the exposure to achieve the image enhancement, create a binary image with threshold method and detect the edge of the image, and use subtraction method for surface defects identification. The experiment indicates that the above methods the advantages of simple, the capability of noise resistance, high speed processing and better real-time.

Zipper classification and defect detection based on computer vision

2020 ◽  
Author(s):  
XinCheng Zhang ◽  
Qing Wang ◽  
JiTong Liu ◽  
ZongAo Liu ◽  
Jun Gong
Keyword(s):  
Computer Vision ◽  
Defect Detection
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