###### Abstract
Machine learning plays an important role in decision-making applications [15]. Also, the use of machine learning algorithms in waste-sorting processing facilities highlights its critical role in managing recyclable materials with robotic technology. These robots demand advanced visual and manipulation capabilities to effectively process diverse recyclable materials within complex industrial environments. Conventional automated systems have historically employed machine/computer vision to extract materials like metal, paper, glass, and plastic from waste streams. Robotic technology [16; 17; 18], positioned as a more efficient and autonomous alternative, can support or replace current installations. In the context of machine vision, the analysis of multi-object images involves processes such as object identification (bounding box specification), localization (masking), and material type classification. This distinctive approach is crafted to precisely identify, locate, and categorize potentially overlapping recyclable materials within the same image, making it particularly suitable for industrial applications. Object detection [19; 20; 21; 22; 23; 24; 25; 26; 27] in deep learning involves both classifying objects into categories (classification) and determining their positions with bounding boxes (localization). This field can be broadly categorized into two main approaches: 1-stage detectors and 2-stage detectors. In 1-stage detectors like YOLO [19; 20; 21; 22] and SSD [23; 24], both the localization and classification tasks are performed concurrently, whereas in 2-stage detectors [25; 26; 27], these tasks are carried out sequentially. The YOLO model [19; 20; 21; 22], being a representative 1-stage detector, has several advantages. Initially, it efficiently processes the entire image at once, eliminating the requirement for separate segmentation and analysis, as used in previous R-CNN-based methods. Secondly, YOLO employs a unified model that amalgamates region proposal, feature extraction, classification, and bounding box regression into one coherent framework, resulting in enhanced speed and facilitating real-time object detection. Thirdly, by operating on the entire image, YOLO effectively captures contextual information about objects and their surroundings, leading to reduced background errors. Lastly, the YOLO model demonstrates remarkable detection accuracy even when dealing with previously unseen images during the training phase, making it well-suited for swift image detection. In this study, the focus lies on utilizing the YOLO detector with a conveyor belt speed of 2m/s to classify plastics, specifically PET and PET-G, in the context of plastic sorting with different shapes. The primary aim of this paper is to explore the recycling of substances with similar compositions but different shapes, which pose challenges for classification using conventional NIRS sorting systems based on chemical composition. To address these challenges,our proposed method incorporates image sensors and object detection deep learning algorithms to enable classification based on the shape and form of the plastics. In particular, with a focus on practical implementation within an industrial setting, a system has been developed through optical design considerations that take into account factors such as sorting speed and conveyor belt characteristics, which are prevalent in the field of plastic waste classification. The anticipated benefits of the proposed method include improved sorting accuracy and purity, reduced misclassification rates, and enhanced recycling efficiency, contributing to a more sustainable and effective plastic waste management system.