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

2025-02-09

AI Summary

To address the challenges of slow inference speed, low accuracy, and poor deployability of deep learning models for coal-gangue detection on industrial edge devices, this paper proposes a lightweight and efficient object detection framework. Methodologically: (i) we design ADown, a lightweight downsampling module; (ii) we construct C2PSA-TriAtt, a cross-layer feature aggregation architecture integrating Triplet Attention; and (iii) we introduce Inner-FocalerIoU, a novel loss function that jointly optimizes localization accuracy and convergence on hard samples. Built upon YOLOv11, the framework adopts ShuffleNetV2 as its backbone and incorporates all three innovations. Experiments demonstrate a state-of-the-art mAP of 99.10%, with a 38% reduction in model size, 41% fewer parameters, 40% lower computational cost (FLOPs), and a 1 ms decrease in per-image inference latency—significantly fulfilling real-time deployment requirements on coal-mine edge devices.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Learning & Optimization for CV
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Improve coal gangue detection speed
• Reduce model size and computational cost
• Enhance detection accuracy on edge devices

Innovation

• Uses ShuffleNetV2 for speed
• Introduces ADown for accuracy
• Implements C2PSA-TriAtt module