## 1 Introduction
### Existing Body of Knowledge and the State of the Art  
Conveyor belts are fundamental to industrial applications, such as recycling, mining, and material handling systems. In BECS systems, conveyor belts are not merely transport mechanisms but integral elements in effectively separating non-ferrous metals. Their reliability, efficiency, and durability are often compromised due to mechanical failures, misalignment, material-induced damage, and fire hazards, making monitoring and maintenance critical for industrial operations. Several studies have examined different material separation techniques to optimize sorting efficiency. [PERSON] and [PERSON] (1991) [PERSON] and [PERSON] (1991) explored electronic separation methods, including optical sorting, radiometric techniques, and X-ray-based methods, to enhance mineral separation efficiency. Their research emphasized preconcentration strategies to reduce processing costs and improve recovery rates. However, their approach focused on electrical and density-based properties rather than materials' physical shape and size, which is a critical factor in BECS performance. Another study, Elemental Characterization of Electronic Waste (2013-2023), reviewed various chemical analysis techniques, such as ICP-MS, ICP-OES, and XRF, to determine the elemental composition of e-waste. These methods are essential for identifying and extracting rare metals, but they fail to address how the physical attributes of materials affect separation efficiency. Unlike these studies, the proposed project integrates machine vision and deep learning (YOLOv11) to analyze material shape and size, significantly improving sorting precision and efficiency in BECS systems. Multiple studies have investigated the structural integrity and damage mechanisms affecting conveyor belts. The study Failure Analysis of Rubber-Textile Conveyor Belts analyzed the irreversible internal damage caused by sharp-edged materials. Researchers used computerized microtomography to examine belt deterioration, demonstrating how repeated impact loads lead to progressive structural weakening. However, this approach is reactive as it only examines failures after they occur. The present study offers a proactive solution, leveraging AI-based detection to prevent damage before it happens. Similarly, Longitudinal Tear Early-Warning in Conveyor Belts Using Infrared Vision introduced a system that detects early signs of belt tears using thermal imaging and Gaussian filtering. While effective in damage detection, it does not prevent hazardous materials from entering the system, a gap that the proposed research aims to address. This project can reduce maintenance costs and operational downtime by identifying and filtering out problematic materials before they reach the belt. Figure 4: Sharp object on the conveyor beltAnother study, Failure Analysis of Belt Conveyor Damage from Falling Materials, used microtomographic imaging to examine the hidden structural damage caused by heavy impacts. While useful for post-damage analysis, this technique lacks industrial applicability. In contrast, the proposed system employs deep learning and machine vision to prevent material-induced damage before it affects the conveyor belt, ensuring continuous, safe operation. Recent advancements in AI-powered monitoring have led to conveyor belt maintenance solutions. The study Proactive Measures to Prevent Conveyor Belt Failures Using Deep Learning introduced an AI-based detection system utilizing a lightweight version of YOLOv4 to identify foreign objects on conveyor belts. While similar to the current project's use of machine vision and deep learning, its primary focus was detecting large objects like metal rods and coal fragments. The present research, however, is tailored for fine material sorting in BECS systems, particularly analyzing shape and size for optimal separation.