MACHINE VISION TECHNOLOGY FOR FOOD QUALITY AND SAFETY INSPECTIONS

With increased expectations for food products of high quality and safety standards, the need for accurate, fast and objective determination of these characteristics in food products continues to grow. Machine vision as a non-destructive technology, provides an automated and economic way to accomplish these requirements. This research thus explored two applications of using machine vision techniques for food quality and safety inspections. The first application is using a combined X-ray and laser range imaging system to detect bone and other physical contaminants inside poultry meat. For this project, our research focuses on how to calibrate the imaging system. A unique three-step calibration method was developed and results showed that high accuracy has been achieved for the whole system calibration - a root mean square error of 0.20 mm, a standard deviation of 0.20 mm, and a maximum error of 0.48 mm. The second application is separating walnuts' shells and meat. A backlight imaging system was developed based on our finding that the backlit images of walnut shells and meat showed quite different texture patterns due to their different light transmittance properties. The texture patterns were characterized by several rotation invariant texture analysis methods. The uncorrelated and redundant features were further removed by a support vector machine (SVM) based recursive feature elimination method, with the SVM classifier trained concurrently for separations of walnuts' shells and meat. The experimental results showed that the proposed approach was very effective and could achieve an overall 99.2% separation accuracy. This high separation accuracy and low instrument cost make the proposed imaging system a great benefit to the walnut processing industry

Deep CNN-Based Automated Optical Inspection for Aerospace Components

ABSTRACT The defect detection problem is of outmost importance in high-tech industries such as aerospace manufacturing and is widely employed using automated industrial quality control systems. In the aerospace manufacturing industry, composite materials are extensively applied as structural components in civilian and military aircraft. To ensure the quality of the product and high reliability, manual inspection and traditional automatic optical inspection have been employed to identify the defects throughout production and maintenance. These inspection techniques have several limitations such as tedious, time- consuming, inconsistent, subjective, labor intensive, expensive, etc. To make the operation effective and efficient, modern automated optical inspection needs to be preferred. In this dissertation work, automatic defect detection techniques are tested on three levels using a novel aerospace composite materials image dataset (ACMID). First, classical machine learning models, namely, Support Vector Machine and Random Forest, are employed for both datasets. Second, deep CNN-based models, such as improved ResNet50 and MobileNetV2 architectures are trained on ACMID datasets. Third, an efficient defect detection technique that combines the features of deep learning and classical machine learning model is proposed for ACMID dataset. To assess the aerospace composite components, all the models are trained and tested on ACMID datasets with distinct sizes. In addition, this work investigates the scenario when defective and non-defective samples are scarce and imbalanced. To overcome the problems of imbalanced and scarce datasets, oversampling techniques and data augmentation using improved deep convolutional generative adversarial networks (DCGAN) are considered. Furthermore, the proposed models are also validated using one of the benchmark steel surface defects (SSD) dataset

A Survey on Audio-Video based Defect Detection through Deep Learning in Railway Maintenance

Within Artificial Intelligence, Deep Learning (DL) represents a paradigm that has been showing unprecedented performance in image and audio processing by supporting or even replacing humans in defect and anomaly detection. The Railway sector is expected to benefit from DL applications, especially in predictive maintenance applications, where smart audio and video sensors can be leveraged yet kept distinct from safety-critical functions. Such separation is crucial, as it allows for improving system dependability with no impact on its safety certification. This is further supported by the development of DL in other transportation domains, such as automotive and avionics, opening for knowledge transfer opportunities and highlighting the potential of such a paradigm in railways. In order to summarize the recent state-of-the-art while inquiring about future opportunities, this paper reviews DL approaches for the analysis of data generated by acoustic and visual sensors in railway maintenance applications that have been published until August 31st, 2021. In this paper, the current state of the research is investigated and evaluated using a structured and systematic method, in order to highlight promising approaches and successful applications, as well as to identify available datasets, current limitations, open issues, challenges, and recommendations about future research directions

Identification of Pecan Weevils Through Image Processing

The Pecan Weevil attacks the pecan nut, causes significant financial loss and can cause total crop failure. A traditional way of controlling this insect is by setting traps in the pecan orchard and regularly checking them for weevils. The objective of this study is to develop a recognition system that can serve in a wireless imaging network for monitoring pecan weevils. Recognition methods used in this study are based on template matching. The training set consisted of 205 pecan weevils and the testing set included 30 randomly selected pecan weevils and 75 other insects which typically exist in a pecan habitat. Five recognition methods, namely, Zernike moments, Region properties, Normalized cross-correlation, String matching, and Fourier descriptors methods were used in this recognition system. It was found that no single method was sufficiently robust to yield the desired recognition rate, especially in varying data sets. It was also found that region-based shape representation methods were better suited inBiosystems and Agricultural Engineerin

Deep Industrial Image Anomaly Detection: A Survey

The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the new setting from industrial manufacturing and review the current IAD approaches under our proposed our new setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection

Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers\u27 expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects\u27 attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import-export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accurate, and reliable insect infestation monitoring systems to replace invasive and subjective methods that are often inefficient. There are still major limitations to the effective in-field, as well as postharvest on-line, monitoring applications. This review presents a general overview of current non-destructive techniques for the detection of insect damage in fruits and vegetables and discusses basic principles and applications. The paper also elaborates on the specific post-harvest fruit infestation detection methods, which include principles, protocols, specific application examples, merits, and limitations. The methods reviewed include those based on spectroscopy, imaging, acoustic sensing, and chemical interactions, with greater emphasis on the noninvasive methods. This review also discusses the current research gaps as well as the future research directions for non-destructive methods\u27 application in the detection and classification of insect infestation in fruits and vegetables

C7orf59/LAMTOR4 phosphorylation and structural flexibility modulate ragulator assembly

Ragulator is a pentamer composed of p18, MP1, p14, C7orf59, and hepatitis B virus X-interacting protein (HBXIP; LAMTOR 1-5) which acts as a lysosomal scaffold of the Rag GTPases in the amino acid sensitive branch of TORC1 signaling. Here, we present the crystal structure of human HBXIP-C7orf59 dimer (LAMTOR 4/5) at 2.9 angstrom and identify a phosphorylation site on C7orf59 which modulates its interaction with p18. Additionally, we demonstrate the requirement of HBXIP-C7orf59 to stabilize p18 and allow further binding of MP1-p14. The structure of the dimer revealed an unfolded N terminus in C7orf59 (residues 1-15) which was shown to be essential for p18 binding. Full-length p18 does not interact stably with MP1-p14 in the absence of HBXIP-C7orf59, but deletion of p18 residues 108-161 rescues MP1-p14 binding. C7orf59 was phosphorylated by protein kinase A (PKA) in vitro and mutation of the conserved Ser67 residue to aspartate prevented phosphorylation and negatively affected the C7orf59 interaction with p18 both in cell culture and in vitro. C7orf59 Ser67 was phosphorylated in human embryonic kidney 293T cells. PKA activation with forskolin induced dissociation of p18 from C7orf59, which was prevented by the PKA inhibitor H-89. Our results highlight the essential role of HBXIP-C7orf59 dimer as a nucleator of pentameric Ragulator and support a sequential model of Ragulator assembly in which HBXIP-C7orf59 binds and stabilizes p18 which allows subsequent binding of MP1-p149915891602CNPQ - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPESP – Fundação de Amparo à Pesquisa Do Estado De São Paulo2014/12445-0; 2017/21455-7; 2014/17264-3190174/2012-

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Development and implementation of real time image analysis algorithms

This work concerns the development and implementation of real-time image processing algorithms. Such systems may be applied to industrial inspection problems, which typically require basic operations to be performed on 256 x 256 pixel images in 20 to 100ms using systems costing less than about &pound;20000.Building such systems is difficult because conventional processors executing at around 1MIPS with conventional algorithms are some 2 orders of magnitude too slow. A solution to this is to use a closely coupled array processor such as the DAP, or CLIP4 which is designed especially for image processing. However such a space-parallel architecture imposes its own structure on the problem, and this restricts the class of algorithms which may be efficiently executed to those exhibiting similar space parallelism, i.e. so-called 'parallel algorithms'. This thesis examines an alternative approach which uses a mix of conventional processors and high speed hardware processors. A special frame store has been built for the acquisition and display of images stored in memory on a multiprocessor backplane. Also described are an interface to a host mini-computer, a bus interface to the system and its use with some hardwired and microcoded processors. This system is compared to a single computer operating with a frame store optimised for image processing. The basic software and hardware system described in this thesis has been used in a factory environment for foodproduct inspection.<p