Computer Vision Based Technique for Surface Defect Detection of Apples

2018 ◽  
pp. 1627-1639
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.

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.

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.

Lightweight detection algorithm for fine-grained surface defects of aerospace seal rings

2021 ◽  
pp. 1-18
Author(s):  
Hui Liu ◽  
Boxia He ◽  
Yong He ◽  
Xiaotian Tao
Keyword(s):  
Feature Extraction ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Algorithm ◽  
Seal Ring ◽  
Fine Grained ◽  
Aerospace Applications ◽  
Surface Defect Detection ◽  
The Mean

The existing seal ring surface defect detection methods for aerospace applications have the problems of low detection efficiency, strong specificity, large fine-grained classification errors, and unstable detection results. Considering these problems, a fine-grained seal ring surface defect detection algorithm for aerospace applications is proposed. Based on analysis of the stacking process of standard convolution, heat maps of original pixels in the receptive field participating in the convolution operation are quantified and generated. According to the generated heat map, the feature extraction optimization method of convolution combinations with different dilation rates is proposed, and an efficient convolution feature extraction network containing three kinds of dilated convolutions is designed. Combined with the O-ring surface defect features, a multiscale defect detection network is designed. Before the head of multiscale classification and position regression, feature fusion tree modules are added to ensure the reuse and compression of the responsive features of different receptive fields on the same scale feature maps. Experimental results show that on the O-rings-3000 testing dataset, the mean condition accuracy of the proposed algorithm reaches 95.10% for 5 types of surface defects of aerospace O-rings. Compared with RefineDet, the mean condition accuracy of the proposed algorithm is only reduced by 1.79%, while the parameters and FLOPs are reduced by 35.29% and 64.90%, respectively. Moreover, the proposed algorithm has good adaptability to image blur and light changes caused by the cutting of imaging hardware, thus saving the cost.

scholarly journals Strategies and facilities in order to improve animal welfare

2010 ◽  
Vol 39 (suppl spe) ◽  
pp. 311-316 ◽  
Author(s):  
Daniella Jorge de Moura ◽  
Leda Globbo de Freitas Bueno ◽  
Karla Andrea Oliveira de Lima ◽  
Thayla Morandi Ridolfi de Carvalho ◽  
Ana Paula de Assis Maia Maia
Keyword(s):  
Animal Welfare ◽  
Animal Behavior ◽  
World Wide ◽  
Litter Quality ◽  
Quality Standards ◽  
Production Costs ◽  
Key Factors ◽  
The Subject ◽  
State Of Art

To keep the position in being a world-wide exporter of chicken meat, Brazil must meet international quality standards, always seeking alternative resources of improvement, without increasing production costs, including litter quality, requirements of animal welfare and environment affairs, such as the use and reuse of broiler litter. Researches are performed in the areas of animal welfare, environment, animal behavior and use of modern climatization technology improving the quality of the environment created to raise broilers, also trying to reduce the greenhouse gas emissions and global warming in the environment, becoming a sustainable production system. This paper has a bibliographic revision of the subject mentioned above, intending to show a state-of-art key factors related to a new concept of broiler environment and welfare.

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.

Image-based Surface Defect Detection Using Deep Learning: A Review

2021 ◽  
pp. 1-23
Author(s):  
Prahar Bhatt ◽  
Rishi K. Malhan ◽  
Pradeep Rajendran ◽  
Brual Shah ◽  
Shantanu Thakar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Surface Defects ◽  
Future Research ◽  
Specific Class ◽  
Survey Paper ◽  
Context Learning ◽  
Surface Defect Detection ◽  
Manufacturing Applications ◽  
Processing Techniques

Abstract Automatically detecting surface defects from images is an essential capability in manufacturing applications. Traditional image processing techniques were useful in solving a specific class of problems. However, these techniques were unable to handle noise, variations in lighting conditions, and background with complex textures. Increasingly deep learning is being explored to automate defect detection. This survey paper presents three different ways of classifying various efforts. These are based on defect detection context, learning techniques, and defect localization and classification method. The existing literature is classified using this methodology. The paper also identifies future research directions based on the trends in the deep learning area.

Machine Vision System for Automatic Inspection of Surface Defects in Aluminum Die Casting

2006 ◽  
Vol 10 (3) ◽  
pp. 281-286 ◽  
Author(s):  
Yakov Frayman ◽  
◽  
Hong Zheng ◽  
Saeid Nahavandi ◽  
Keyword(s):  
Genetic Algorithm ◽  
Machine Vision ◽  
Defect Detection ◽  
Surface Defects ◽  
Vision System ◽  
Die Casting ◽  
Noise Removal ◽  
Morphological Operations ◽  
Automatic Inspection ◽  
Machine Vision System

A camera based machine vision system for the automatic inspection of surface defects in aluminum die casting is presented. The system uses a hybrid image processing algorithm based on mathematic morphology to detect defects with different sizes and shapes. The defect inspection algorithm consists of two parts. One is a parameter learning algorithm, in which a genetic algorithm is used to extract optimal structuring element parameters, and segmentation and noise removal thresholds. The second part is a defect detection algorithm, in which the parameters obtained by a genetic algorithm are used for morphological operations. The machine vision system has been applied in an industrial setting to detect two types of casting defects: parts mix-up and any defects on the surface of castings. The system performs with a 99% or higher accuracy for both part mix-up and defect detection and is currently used in industry as part of normal production.

A Review Paper on Product Surface Defect Detection of Ironing Process

2016 ◽  
Vol 836 ◽  
pp. 147-152
Author(s):  
Akhmad Faizin ◽  
Arif Wahjudi ◽  
I. Made Londen Batan ◽  
Agus Sigit Pramono
Keyword(s):  
Image Processing ◽  
Surface Roughness ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Forming Process ◽  
Metal Forming Process ◽  
Surface Defect Detection ◽  
Product Surface ◽  
Processing Techniques

The quality of product of manufacturing industries depends on dimension accurately and surface roughness quality. There are many types of surface defects and levels of surface roughness quality. Ironing process is one type of metal forming process, which aims to reduce the wall thickness of the cup-shaped or pipes products, thus increasing the height of the wall. Manually surface inspection procedures are very inadequate to ensure the surface in guaranteed quality. To ensure strict requirements of customers, the surface defect inspection based on image processing techniques has been found to be very effective and popular over the last two decades. The paper has been reviewed some papers based on image processing for defect detection. It has been tried to find some alternatives of useful methods for product surface defect detection of ironing process.

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