Analysis Of Personal Protective Equipment Classification Method Using Deep Learning

Personal Protective Equipment (PPE) is equipment worn in the workplace in order to reduce the exposure to hazards that causing serious injuries and illness to people. In this project, there are five types of PPE to be considered which is also recognized by Occupational Safety and Health Administration (OSHA) such as face mask, face shield, safety goggle, safety helmet and safety jacket. To avoid a tedious work in manually checking whether workers wear PPE or not, an automatic PPE classifier is constructed by utilizing a deep learning algorithm called Convolutional Neural Network (CNN). This classification is performed using Anaconda and Jupyter Notebok Software that use Python as the programming language. There are total of 7500 images in the PPE dataset, 6000 images for training with and another 1500 images for testing. The classification is done in two ways, one is by classifying the PPE into 5 classes and another one is by classifying into binary class after the best combined parameters are obtained using multiple training and testing by changing the parameters such as epoch, activation function, optimizer and filter layer. By using classifying into 5 classes, the final training accuracy is 89.39% and testing accuracy of 62.23%. On the other hand, by classifying the PPE into binary class, the PPE has final testing accuracy up 88% for all PPE. Face mask has final accuracy of 95.60%, face shield 94.32%, safety goggle 89.79%, safety helmet 98.90% and lastly safety jacket has 88.45% testing accuracy. Based on the result, CNN algorithm is a good algorithm as the binary classification of PPE achieved high accuracy result

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well