一种基于深度学习的煤矸石检测方法

A method of coal gangue detection based on deep learning

针对选煤场的煤矸分离中基于计算机视觉的煤矸石检测方法需要复杂的人工特征设计过程,在YOLOv3目标检测模型基础上,提出一种基于深度学习的端到端煤矸石检测方法。采用深度可分离卷积以及转置卷积对模型的骨干网络进行改进,以缩减模型大小并提高模型运行速度;加入空间金字塔池化模块,改善模型的特征融合能力;引入平衡L1损失函数和距离交并比损失函数,加速模型收敛并提高定位准确性。研究结果表明,所提算法能够实时精准地检测出煤与矸石混合体中的矸石,为提高煤炭质量、改进分拣效率提供有效保障。

In view of the requirement to separate gangue from coal in coal preparation factory and avoid complex artificial feature design process based on computer vision in the past,an end-to-end gangue detection method by deep learning based on YOLOv3 is proposed.In order to reduce the size and speed up the operation of YOLOv3,depth-wise separable convolution and transpose convolution are used to improve the backbone network of YOLOv3.In order to improve feature integration from different levels,the spatial pyramid pooling layer is added to the model.For the purpose of accelerating convergence and accurately locating objects in the model,balanced L1 loss and distance-IoU loss are introduced.The results show that the proposed algorithm can accurately detect the gangue from the mixture of coal and gangue in real time,which provides an effective guarantee for improving the quality of coal and sorting efficiency.