Bread Browning Stage Classification Model using VGG-16 Transfer Learning and Fine-tuning with Small Training Dataset

Convolutional neural network (CNN) is a popular tool to recognize image features even though its weakness requirement of massive training dataset. However, the implementation of the network in production process needs to worry about, that is, the deal with at least two constraints, small training dataset and the less diversity of browning stages among the bread production batches. This paper is aimed to achieve a high predictive accuracy model to classify the bread browning stage that is capable to deal with these constraints. With small training dataset of 900 original images from a production batch, the research performs five steps, starting with a few convolutional layers, adding image augmentation technique and transfer learning with pre-trained CNN model to enhance feature extraction with fine-tuning in final step. The final model of VGG-16 transfer learning and fine-tuning, trained with 18,000 artificial images, successfully achieves very high training accuracy of 98.89% and a very low loss of 2.86% at a small number of epochs 30 with its predictive accuracy of 100%

Studies of inspection algorithms and associated microprogrammable hardware implementations

This work is concerned with the design and development of real-time algorithms for industrial inspection applications. Rather than implement algorithms in dedicated hardware, microprogrammable machines were considered essential in order to maintain flexibility. After a survey of image pattern recognition where algorithms applicable to real-time use are cited, this thesis presents industrial inspection algorithms that locate and scrutinise actual manufactured products. These are fast and robust - a necessary requirement in industrial environments. The National Physical Laboratory have developed a Linear Array Processor (LAP) specifically designed for industrial recognition work. As with most array processors, the LAP has a greater performance than conventional processors, yet is strictly limited to parallel algorithms for optimum performance. It was therefore necessary to incorporate sequentialism into the design of a multiprocessor system. A microcoded bit-slice Sequential Image Processor (SIP) has been designed and built at RHBNC in conjunction with the NPL. This was primarily intended as a post-processor for the LAP based on the VMEbus but in fact has proved its usefulness as a stand-alone processor. This is described along with an assembler written for SIP which translates assembly language mnemonics to microcode. This work, which includes a review of current architectures, leads to the specification of a hybrid (SIMD/NIMD) architecture consisting of multiple autonomous sequential processors. This involves an analysis of various configurations and entails an investigation of the source of bottlenecks within each design. Such systems require a significant amount of interprocessor communication: methods for achieving this are discussed, some of which have only become practical with the decrease incost of electronic components. This eventually leads to a system for which algorithm execution speed increases approximately linearly with the number of processors. The algorithms described in earlier chapters are examined on the system and the practicalities of such a design are analysed in detail. Overall, this thesis has arrived at designs of programmable real-time inspection systems, and has obtained guidelines which will help with the implementation of future inspection systems.<p

Impact Assessment of SPC Tools on Quality Improvement in Pakistani Industrial Environment: A Dynamic Case Study

Whether one belongs to service or production industry, Quality is always a big issue for business persons and the customers. If we do not have any means of measuring the performance of manufacturing Unit in trouble, how can we improve it? As W. Edwards Deming said “If you cannot measure it, you cannot improve it”. Everybody in the field of TQM is familiar with control charts and statistical process improvement for Quality. A number of tools, including the Six Sigma tool box, are made up of seven simple tools: flow chart, check list, histogram, Pareto chart, cause and effect diagram, scatter diagram, control chart. The Japanese call them "seven QC (quality control) tools, which have been used for decades to support quality improvement efforts to solve the problem. Usually variation is the only main reason for varying or low quality of their product/service, increasing dissatisfaction among customers and decreasing business credibility as a result. For that purpose, we chosen a business organization “Silver Lake Foods Pvt. Ltd.” as a study object, as management of SLFL was highly willing to cooperate. SLFL is a Food Manufacturing organization which produces food items like Biscuits, toffees, candies and chocolates, drinks etc. Management of SLFL has found that the process average for critical characteristics i.e. weight, taste etc. were out of control and causing big losses. In some cases, they have some ideas about possible causes. However, in most cases, they do not want or lack of knowledge and resources restrict them to carry out experimental design to find out the reasons for the change or decline quality.We have decided to use statistical process control (SPC) procedures for quality control, quality improvement and then ultimately towards total quality management.There were some ideas about possible causes but, as in most cases, they were reluctant or lacking the knowledge and resources to perform experimental design to find out the causes of variation or the causes of decreased (/ing) quality. We decided to use statistical process control (SPC) program to make the steps towards quality control and from quality control to quality improvement and then ultimately towards TQM. The Proposed study intends to find out impact of SPC tools in Quality improvement in Pakistani Industrial environment by studying Silver Lake Foods Pvt. Ltd. This  research also identify the main  sources of variations and bottlenecks  through dynamic use of SPC tools and  suggest recommendations regarding higher quality improvement and customer satisfaction levels in future. Keywords: Six Sigma tool box, TQM, Customer satisfaction, SPC tools, SLFP (Silver Lake Foods Pvt. Limited), Food industry, Quality improvemen

Automatic detection of dispersed defects in resin eyeglass based on machine vision technology

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Studies of algorithms and related imaging techniques for industrial inspection

This thesis will deal with algorithms and imaging techniques for use in automated industrial inspection. The work falls into two main areas, the first dealing with general problems relating to typical inspection tasks, the second with specific applications including the analysis of seals on plastic packets.The requirements of a general object location and inspection system will be discussed initially in relation to algorithms supplied with commercial systems, which often seem ad-hoc. This will be followed up with detailed analyses of several corner and small hole detection algorithms. The features looked for in a useful algorithm are: (1) a high execution speed when implemented on a general purpose microcomputer, (2) good accuracy in locating the desired features, (3) robustness when faced with poor quality, noisy or cluttered images and (4) the ability to distinguish between genuine features and others that appear, superficially, to be similar. A program using these feature detectors to locate partially occluded machine parts in typical images will be presented.The second main area of investigation is that of the detection of faults in heat sealed food packets and is one which has hitherto largely been overlooked. The main problem with these packets is that the cellophane wrapper is highly reflective, giving rise to large areas of glare in any off-camera image. Experience has shown that careful lighting arrangement alone will never totally remove this problem. However, a simple arrangement of switched light beams, along with computer processing, can almost totally eliminate the glare. This approach has been used in the inspection of packets where faults are revealed by parts of the product inside showing through holes in the wrapper. Alternatively, by careful alignment of the light sources, the surface structure of the sealed part of a packet may be revealed. This can reveal defects either through the absence of a regular pattern, or by the presence of wrinkles running across the seal. Algorithms have been developed demonstrating each of these inspection tasks.Overall the work presented in this thesis has spanned several traditional areas of interest, and has also developed the techniques required for packet inspection and other situations where glare is a problem.<p

What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation

While semantic segmentation has seen tremendous improvements in the past, there is still significant labeling efforts necessary and the problem of limited generalization to classes that have not been present during training. To address this problem, zero-shot semantic segmentation makes use of large self-supervised vision-language models, allowing zero-shot transfer to unseen classes. In this work, we build a benchmark for Multi-domain Evaluation of Semantic Segmentation (MESS), which allows a holistic analysis of performance across a wide range of domain-specific datasets such as medicine, engineering, earth monitoring, biology, and agriculture. To do this, we reviewed 120 datasets, developed a taxonomy, and classified the datasets according to the developed taxonomy. We select a representative subset consisting of 22 datasets and propose it as the MESS benchmark. We evaluate eight recently published models on the proposed MESS benchmark and analyze characteristics for the performance of zero-shot transfer models. The toolkit is available at https://github.com/blumenstiel/MESS