A “Hardware-Friendly” Foreign Object Identification Method for Belt Conveyors Based on Improved YOLOv8
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Belt Conveyor Systems Engineering · Advanced Neural Network Applications · Power Line Inspection Robots
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Shanxi Provincial Engineering Laboratory for Mine Fluid Control, Taiyuan 030024, China
Shandong Libo Heavy Industry Technology Co., Ltd., Taian 271025, China
Funding
National Natural Science Foundation of China · Taishan Industry Leading Talent Program · Key R&D Plan Projects in Shanxi Province · Unmanned Management System for Belt Conveyors Based on Multiple Perception Technology · Shanxi Science Administration for Market Regulation
References (33)
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