###### Abstract  
As a crucial element in coal transportation, conveyor belts play a vital role, and monitoring their health is essential for the coal mine transportation system's safe and efficient operation. This paper introduces a new 'hardware-friendly' method for monitoring belt conveyor damage, aiming to address the issue of large parameters and computational requirements in existing deep learning-based foreign object detection methods and their challenges in deploying on edge devices with limited computing power. This method is tailored towards edge computing and aims to reduce the parameters and computational load of foreign object recognition networks deployed on edge computing devices. This method improves the YOLOv8 object detection network and redesigns a novel lightweight ShuffleNetV2 network as the backbone network, making the network more delicate in recognizing foreign object features while reducing redundant parameters. Additionally, a simple parameter-free attention mechanism called SimAM is introduced to further enhance recognition efficiency without imposing additional computational burden. Experimental results demonstrate that the improved foreign object recognition method achieves a detection accuracy of 95.6% with only 1.6 M parameters and 4.7 G model computational load (FLOPs). Compared to the baseline YOLOv8n, the detection accuracy has improved by 3.3 percentage points, while the number of parameters and model computational load have been reduced by 48.4% and 42.0%, respectively. These works are more friendly to edge computing devices that tend to \"hardware friendly\" algorithms. The improved algorithm can reduce latency in the data transmission process, enabling the accurate and timely detection of non-coal foreign objects on the conveyor belt. This provides assurance for the subsequent host computer system to promptly identify and address foreign objects, thereby ensuring the safety and efficiency of the belt conveyor. b 2023 12 August 2023 8 October 2023  
aligning with the development requirements proposed by Industry 4.0, transitioning towards intelligent and energy-efficient directions [5,6]. The safe operation of the belt conveyor system relies on the normal and healthy functioning of its equipment. Abnormal working conditions can not only cause unnecessary damage or wear to the equipment but also increase the system's energy consumption, leading to additional safety risks and economic pressure for coal mining enterprises, thereby severely impacting the green and sustainable development of the enterprise [7]. In practical production processes, coal often needs to be transported over long distances. Conveyor belts efficiently facilitate the movement of large quantities of coal from one location to another, ensuring the unobstructed flow of the coal supply chain. Additionally, conveyor belt systems, meticulously engineered, enable the automated and continuous transportation of coal, eliminating the need for extensive manual labor. This not only enhances production efficiency but also reduces labor costs while minimizing the potential for human errors. Given the intricate working environment of belt conveyors, issues such as belt misalignment, belt tearing, and overloading [8] frequently manifest, with belt damage being the most prevalent among these malfunctions. Investigation has revealed that longitudinal tearing occurs when the belt encounters external sharp objects, such as anchor rods, large chunks of rock, angle irons, and iron plates [9, 10, 11]. Failure to promptly detect and address non-coal foreign objects on the conveyor belt may result in abnormal stoppages and conveyor belt malfunctions, introducing a gamut of potential risks and losses. These may include diminished production efficiency, the loss of substantial production capacity, delays in the transportation of coal and other commodities, and disruptions in the logistics of the supply chain, affecting delivery schedules and production plans.