基于SCI-XDNet-CFF轻量化网络的井下运煤皮带异物识别
Identification of Foreign Objects in Underground Coal Transportation Belt Based on SCI-XDNet-CFF Lightweight Networks
孙亚琳 1孙鹏翔 1薛晔 1刘泽宇 2孙贵有2
作者信息
- 1. 太原理工大学经济管理学院,山西 太原 030000
- 2. 山西汾西矿业集团正新煤焦有限责任公司,山西 长治 046512
- 折叠
摘要
矿井煤炭开采面与地面距离较长,需要通过运煤皮带进行长距离运输,在运输过程中,存在大块矸石、锚杆等异物损坏皮带、堵塞落煤口的问题,易引发安全问题,因此,运煤皮带运输异物分类具有重要意义.为克服井下环境光照强度弱、识别精度低、模型参数量大的问题,提出一种结合低光照图像增强的XDNet-CFF轻量化网络.首先,采用预训练的自校准光照图像增强模型对井下运煤皮带图像进行低光照图像增强,有效提高图像质量;其次,设计一种基于Xcpetion-DenseNet121和跨层特征融合的深度网络,在提高特征提取能力的同时,将底层细节特征与上层语义特征相结合,减少信息丢失,丰富特征表示;然后,通过全连接层和softmax完成运煤皮带异物识别;最后,为实现移动端部署和识别预警,应用剪枝方法对模型进行压缩,大幅减少模型参数量,降低开销.结果表明,所提模型在运煤皮带异物数据集上准确率、精度、召回率、F1分数分别达到 0.946 7、0.951 2、0.941 6、0.946 4,均优于主流模型,同时,参数量仅8.98 M,满足实际生产部署需求.
Abstract
The coal mining face of the mine has a long distance from the ground,and needs to be transported over long distances by coal transport belts,and in the process of transport,there are problems such as large gangue,anchors and other foreign objects damaging the belts and blocking the coal drop openings,which are prone to cause safety problems,so the classification of foreign objects in the transport of coal transport belts is of great significance.In order to overcome the problems of weak light intensity,low recognition accuracy and large number of model parameters in the underground environment,an XDNet-CFF lightweight network combined with low-light image enhancement is proposed.Firstly,a pre-trained self-calibrated light image enhancement model is used to perform low-light image enhancement on underground coal transport belt images to effectively improve the image quality;secondly,a deep network based on Xcpetion-DenseNet121 and cross-layer feature fusion is designed to improve the feature extraction capability while combining the underlying detail features with the upper layer semantic features to reduce the loss of information and enrich the feature representation;then,the coal belt foreign object recognition is completed by fully connected layer and softmax;finally,in order to achieve mobile deployment and recognition warning,the model is compressed by applying pruning method,which significantly reduces the number of model parameters and overheads.The results show that the proposed model achieves accuracy,precision,recall and F1 score of 0.946 7,0.951 2,0.941 6,0.946 4 on the coal belt foreign object dataset,which are better than the mainstream model,and at the same time,the number of references is only 8.98 M,which meets the demand of actual production deployment.
关键词
低光照图像增强/XDNet-CFF/跨层特征融合/运煤皮带/异物识别Key words
Low-light image enhancement/XDNet-CFF/cross-layer feature fusion/coal transport belt/foreign object recognition引用本文复制引用
出版年
2025
NETL
NSTL
万方数据