Automatic Waste Sorting In Industrial Environments Via Machine Learning Approaches

Speed, safety and efficiency are the key to any industrial progress. We as human beings, get astounded by the industrial achievements and the products manufactured, but we tend to forget about the residue and waste it leaves behind. As the saying goes “ One man's trash is another man's treasure”, we can make use of the waste to generate energy by heat in an incineration plant, recycle it to save natural resources and reduce pollution by effectively recycling inhouse waste therefore decrease in products reaching in landfills. To make the recycling process effective, we have to overcome challenges such as the slow pace of manual sorting, mixing of different materials due to ineffective sorting and labor exposed to harmful materials, which is where automated waste sorting using image-based classification comes into play. The objective of this thesis is to deter- mine and study how we can use different machine learning algorithms, such as convo- lution neural network (CNN) and support vector machines (SVM) to effectively classify waste generated in the industrial environment into three categories: paper, plastic and metal. We initiated this thesis work to evaluate if MATLAB with its extensive range of toolboxes can make the image classification task easier, user-friendly and practical. We applied image processing toolbox to preprocess the data, computer vision toolbox to implement images detection and so on. Pictures of the waste types were acquired using TrashNet dataset and the Internet. This thesis does not purpose a new classification methodology. It rather aims at designing practical algorithms to work on large-scale data sets to achieve better image classification than the current approaches. We performed simulation with both CNN and SVM image classifier using three different datasets with 200, 400 and 600 images in each category with image sizes (32x32,64x64, 128x128), comparing different layer configurations, evaluating other opti- mizer and kernel functions. As a result, an efficient and accurate model was developed. The bag of features was used to extract robust features in the case of SVM. CNN per- formed better than SVM, reaching 82.2% accuracy, whereas 79.4% was the highest ac- curacy achieved by SVM Even though we achieved some good result, there is still room for improvement. Also identifying the components in a hybrid waste (e.g., combinations of paper, plastic, and metal) remains as a topic of future research