轻量化模型提升煤矸石检测速度与精度，适合工业边缘部署。
SNAT-YOLO: Efficient Cross-Layer Aggregation Network for Edge-Oriented Gangue Detection
- 用ShuffleNetV2替代主干网络，结合ADown下采样减少计算开销。
- 引入三重注意力机制的C2PSA-TriAtt模块，检测准确率达99.10%。
- 模型体积缩小38%，参数减少41%，可在边缘设备高效运行。
针对基于深度学习的煤矸石目标检测方法存在检测速度慢、精度低、难以在工业边缘设备部署及参数量与计算量大的问题，本文提出一种基于改进YOLOv11的轻量化煤矸石目标检测算法。首先，采用轻量级网络ShuffleNetV2作为主干网络以提升检测速度；其次，引入轻量级下采样操作ADown，降低模型复杂度的同时提高平均检测精度；第三，通过融合三重注意力机制改进YOLOv11中的C2PSA模块，提出C2PSA-TriAtt模块，增强模型对图像多维度特征的关注能力；第四，提出Inner-FocalerIoU损失函数替代原有CIoU损失函数。实验结果表明，该模型在煤矸石检测任务中达到99.10%的检测准确率，模型大小减少38%，参数量减少41%，计算成本降低40%，单图平均检测时间减少1毫秒。改进后的模型具备更强的检测速度与精度，适用于工业边缘移动设备部署，有助于提升煤炭加工效率与资源利用。
原文摘要 · Abstract (English)
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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