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

• URL: http://arxiv.org/abs/2502.05988v2
• Date: Tue, 18 Feb 2025 09:11:16 GMT
• Title: SNAT-YOLO: Efficient Cross-Layer Aggregation Network for Edge-Oriented Gangue Detection
• Authors: Shang Li,
• Abstract summary: Our model achieves a detection accuracy of 99.10% in coal gangue detection tasks. It 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.
• Score: 1.7948767405202701
• License:
• 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.

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