Lightweight Coal Flow Foreign Object Detection Algorithm
Authors: 
Ru Nie, Xiaobing Shen, Zhengwei Li, Yanxia Jiang, Hongmei Liao, Zhuhong YouAuthors Info & Claims
Pages 393 - 404
Abstract
In response to the challenges of foreign object detection and high real-time requirements in complex coal mine monitoring scenarios, a lightweight coal flow foreign object detection algorithm, FAI_YOLO, was developed based on YOLOv8s, which incorporates several innovations to address these challenges. Initially, Fastnet is employed as the backbone feature extraction network to minimize redundant computation and memory access, thereby accelerating inference speed. Additionally, AKConv replaces the traditional convolution operation in C2f module, and the loss function ImpIOU is refined to enhance regression performance. Experimental results suggest that this algorithm markedly improves the speed of foreign object detection in coal flow compared to the original YOLOv8s model, decreasing frame reasoning time by 33.8%. While the [email protected] metric experiences a minor decrease of 0.03%, the algorithm continues to provide high detection accuracy and effectively manages the demands of real-time and accurate foreign object detection in complex coal mine monitoring scenarios.
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Published In
Aug 2024
535 pages
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 13 August 2024
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