Sistema de clasificación de botellas mediante el reconocimiento de imágenes para el tratamiento de basura. - Loaiza Alejandro, Bryan José (2024)
The objective of this work is to create a prototype for the classification of bottles, whether they are made of glass or plastic or any other material, using image recognition. The prototype consists of two parts, the first corresponds to a vibrating table to disperse the garbage. In contrast, the second part corresponds to a conveyor belt where the detection and classification system of the bottles is installed. The model used for the detection is YOLOv5s, the code for the detection is written in Python language, while the code for the sorting is developed through the arduino IDE and the ESP32 microcontroller. The prototype is done in three phases. In the first phase, the state of the art is studied, as well as the analysis of mechanisms and algorithms. In the second phase, the detection model is trained, the respective codes are developed and the physical part of the project is elaborated. Finally, phase three integrates the two parts and proceeds to data collection. After 2700 tests to analyze its behavior in different scenarios, it was determined that the prototype tends to make more errors in detection and classification as the number of bottles entering from the vibrating table increases. However, after the statistical analysis applied, it was concluded that the designed prototype has a high level of accuracy in the detection and classification of the bottles.