## 1 Introduction
### Gap Detection  
Previous research on the performance and safety of industrial conveyor belts has primarily focused on analyzing physical damage and predicting failures. Many studies have examined traditional methods, such as microtomographic analysis, magnetic sensors, X-ray imaging, and infrared monitoring systems, to detect conveyor belt failures [44; 45].  
While these methods effectively identify physical damage after it has occurred, they do not provide a preventive approach to stop harmful materials from reaching the conveyor belt in the first place. This represents a significant research gap as the primary focus of existing studies has been on damage identification and analysis rather than proactive prevention.  
In several industries, particularly in ECS systems, preventing such failures can reduce maintenance costs and enhance system efficiency, yet this aspect remains largely unexplored. Studies that have investigated intelligent monitoring systems have primarily focused on detecting conveyor belt misalignment or identifying large foreign objects, such as screws and metal rods [44; 45]. These research efforts have employed deep-learning models like YOLOv4 and YOLOv5 for object detection.  
However, they do not address the critical issue of small material characteristics and their impact on non-ferrous metal separation accuracy. In ECS systems, improperly shaped or oversized materials (such as sharp-edged or irregularly shaped pieces) negatively affect separation efficiency and can cause mechanical damage to the conveyor belt and other system components [22].  
While previous research has advanced object detection models, no study has specifically targeted a shape and size analysis of trim materials and their influence on the ECS process. Another significant research gap in conveyor belt safety relates to fire hazard detection and temperature monitoring. Existing studies have utilized gas sensors, smoke detectors, and Distributed Temperature Sensing (DTS) systems for monitoring conveyor belt temperatures [36; 37; 38; 39; 40; 41].  
However, these systems are expensive, require frequent maintenance, and often underperform in dusty industrial environments. Additionally, these methods only record temperature data without identifying the cause of temperature increases. Moreover, the use of thermal cameras as a monitoring tool in industrial conveyor belt systems has been relatively underexplored. In prior research, thermal cameras were mainly employed for fire detection in open environments, such as forests [48].  
In contrast, this project applied thermal imaging to continuously monitor conveyor belt temperatures and prevent the fire hazards caused by ferromagnetic contamination in ECS systems [42; 43].  
Overall, the primary research gaps addressed in this project include the lack of an intelligent system for preventing conveyor belt damage instead of merely detecting it, the absence of studies focusing on the impact of material shape and size in ECS systems, and the need for a comprehensive solution to identify and prevent temperature increases in industrial conveyor belts. By combining computer vision, deep learning, and IoT-based automation, this project introduces an approach that reduces physical damage and maintenance costs and enhances separation accuracy and overall system efficiency.