Shape and Level Bottles Detection Using Local Standard Deviation and Hough Transform

This paper presents shape and level analysis using local standard deviation and Hough transform technique to detect the shape and level of the bottle.A 155 sample images are used as a test product to detect shape and level. Local standard deviation is used contrast gain technique to segment the shape of the bottle by enhancing the contrast of the image. The ratio of the area is calculated from the extent parameter. The maximum and minimum water level is created by using Hough transform technique to identify the level of the water. Decision tree is applied to classify the shape and level of the bottle either good or defect condition. From experimental result, 97% and 93% accuracy of shape and level is achieved which shows that the proposed analysis technique is potential to be applied for beverages product inspection system

A Review Of Vision Based Defect Detection Using Image Processing Techniques For Beverage Manufacturing Industry

Vision based quality inspection emerged as a prime candidate in beverage manufacturing industry. It functions to control the product quality for the large scale industries; not only to save time, cost and labour, but also to secure a competitive advantage. It is a requirement of International Organization for Standardization (ISO) 9001, to appease the customer satisfaction in term of frequent improvement of the quality of products and services. It is totally impractical to rely on human inspector to handle a large scale quality control production because human has major drawback in their performance such as inconsistency and time consuming. This article reviews defect detection using image processing techniques for beverage manufacturing industry. There are comparative studies on techniques suggested by previous researchers. This review focuses on shape defect detection, color concentration inspection and level of liquid products measurement in a container. Shape, color and level defects are the main concern for bottle inspection in beverage manufacturing industry. The development of practical testing and the services performance are also discussed in this paper

Automated Vision-Based Beverage Bottle Quality And Level Inspection System

Automated vision inspection emerged as an important part of the product quality monitoring process.It is a requirement of International Organization for Standardization (ISO) 9001 to appease the customer satisfaction in terms of frequent improvement of the quality of products.It is totally impractical to rely on human inspector to handle a large scale quality control production because human has major drawback in their performance such as inconsistency and time consuming. Therefore,an automatic inspection is a promising approach to maintain product quality as well as to resolve the existing problems relate to delay outputs and cost burden. This research presents a computerized analysis to detect defects occur in beverages production in order to minimize the defective products.Image processing techniques are proposed to detect defects of beverages bottle.The defects are categorized into three classes which are bottle shape defect, color concentration defect and liquid level defect.For shape defect detection,three techniques are proposed namely local standard deviation (LSD),morphological operation and adaptive thresholding. Statistical histogram,gray level co-occurrence matrix (GLCM) and quadratic distance are applied for color concentration defect detection. The liquid level is detected using Hough transform and coordinate of point techniques. The classification process is analyzed using rule-based and decision tree classifiers.In developing automated beverage bottle quality and level inspection system, the performance is verified in terms of accuracy.The simulation result demonstrate LSD,statistical histogram and Hough transform are selected as the best technique by achieving 98% of shape,93% of color concentration and 91% of liquid level. For the system result,93% average accuracy has achieved for three defect detections. The system is ready for internet of things (IoT) platform which is using raspberry pi that gives benefit to user for wirelessly access and monitor the results.For the results validation,field testing is conducted,and the proposed system shows the capability to classify the bottle defect accurately.Thus,it has proven the proposed system is appropriate to be implemented in real-time application for beverage bottle quality inspection

Real-time Product Quality Inspection Monitoring System using Quadratic Distance and Level Classifier

Automated product quality inspection has become a very important process in industries to maintain high product efficiency. This paper presents a real-time product quality inspection monitoring system for beverages product. The proposed system used Internet Protocol (IP) camera to capture the image of the bottle through computer network in order to inspect color concentration and water level of the bottle. Quadratic distance technique is applied for color concentration analysis based on a combination of Red, Green and Blue (RGB) histogram. The vertical and horizontal coordinates technique is used to inspect three conditions of the level, which are passed, overfill and underfill. The proposed system has achieved 100% accuracy using 246 samples

Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm

Background/Purpose. In terms of the detection of tooth diagnosis, no intelligent detection has been done up till now. Dentists just look at images and then they can detect the diagnosis position in tooth based on their experiences. Using new technologies, scientists will implement detection and repair of tooth diagnosis intelligently. In this paper, we have introduced one intelligent method for detection using particle swarm optimization (PSO) and our mathematical formulation. This method was applied to 2D special images. Using developing of our method, we can detect tooth diagnosis for all of 2D and 3D images. Materials and Methods. In recent years, it is possible to implement intelligent processing of images by high efficiency optimization algorithms in many applications especially for detection of dental caries and restoration without human intervention. In the present work, we explain PSO algorithm with our detection formula for detection of dental caries and restoration. Also image processing helped us to implement our method. And to do so, pictures taken by digital radiography systems of tooth are used. Results and Conclusion. We implement some mathematics formula for fitness of PSO. Our results show that this method can detect dental caries and restoration in digital radiography pictures with the good convergence. In fact, the error rate of this method was 8%, so that it can be implemented for detection of dental caries and restoration. Using some parameters, it is possible that the error rate can be even reduced below 0.5%

Improving the safety and efficiency of rail yard operations using robotics

textSignificant efforts have been expended by the railroad industry to make operations safer and more efficient through the intelligent use of sensor data. This work proposes to take the technology one step further to use this data for the control of physical systems designed to automate hazardous railroad operations, particularly those that require humans to interact with moving trains. To accomplish this, application specific requirements must be established to design self-contained machine vision and robotic solutions to eliminate the risks associated with existing manual operations. Present-day rail yard operations have been identified as good candidates to begin development. Manual uncoupling, in particular, of rolling stock in classification yards has been investigated. To automate this process, an intelligent robotic system must be able to detect, track, approach, contact, and manipulate constrained objects on equipment in motion. This work presents multiple prototypes capable of autonomously uncoupling full-scale freight cars using feedback from its surrounding environment. Geometric image processing algorithms and machine learning techniques were implemented to accurately identify cylindrical objects in point clouds generated in real-vi time. Unique methods fusing velocity and vision data were developed to synchronize a pair of moving rigid bodies in real-time. Multiple custom end-effectors with in-built compliance and fault tolerance were designed, fabricated, and tested for grasping and manipulating cylindrical objects. Finally, an event-driven robotic control application was developed to safely and reliably uncouple freight cars using data from 3D cameras, velocity sensors, force/torque transducers, and intelligent end-effector tooling. Experimental results in a lab setting confirm that modern robotic and sensing hardware can be used to reliably separate pairs of rolling stock up to two miles per hour. Additionally, subcomponents of the autonomous pin-pulling system (APPS) were designed to be modular to the point where they could be used to automate other hazardous, labor-intensive tasks found in U.S. classification yards. Overall, this work supports the deployment of autonomous robotic systems in semi-unstructured yard environments to increase the safety and efficiency of rail operations.Mechanical Engineerin

2015 IMSAloquium, Student Investigation Showcase

We want to express our gratitude for the generosity and steadfast support of all the experts and leaders who have nurtured These collaborative partnerships are the strength of our SIR program.https://digitalcommons.imsa.edu/archives_sir/1014/thumbnail.jp