SNAT-YOLO: Efficient Cross-Layer Aggregation Network for Edge-Oriented Gangue Detection

TL;DR

This paper introduces SNAT-YOLO, a lightweight, efficient coal gangue detection model based on an improved YOLOv11 architecture, optimized for edge devices with high accuracy and reduced computational requirements.

Contribution

It proposes a novel combination of lightweight backbone, downsampling, attention mechanism, and loss function to enhance detection speed and accuracy on industrial edge devices.

Findings

Achieves 99.10% detection accuracy

Reduces model size by 38% and parameters by 41%

Decreases detection time per image by 1 ms

Abstract

To address the issues of slow detection speed,low accuracy,difficulty in deployment on industrial edge devices,and large parameter and computational requirements in deep learning-based coal gangue target detection methods,we propose a lightweight coal gangue target detection algorithm based on an improved YOLOv11.First,we use the lightweight network ShuffleNetV2 as the backbone to enhance detection speed.Second,we introduce a lightweight downsampling operation,ADown,which reduces model complexity while improving average detection accuracy.Third,we improve the C2PSA module in YOLOv11 by incorporating the Triplet Attention mechanism,resulting in the proposed C2PSA-TriAtt module,which enhances the model's ability to focus on different dimensions of images.Fourth,we propose the Inner-FocalerIoU loss function to replace the existing CIoU loss function.Experimental results show that our model achieves a detection accuracy of 99.10% in coal gangue detection tasks,reduces the model size by 38%,the number of parameters by 41%,and the computational cost by 40%,while decreasing the average detection time per image by 1 ms.The improved model demonstrates enhanced detection speed and accuracy,making it suitable for deployment on industrial edge mobile devices,thus contributing positively to coal processing and efficient utilization of coal resources.