Automatic Recognition of Coal and Gangue based on Convolution Neural Network

Automatic Identification of Coal and Rock/Gangue Based on DenseNet and Gaussian Process

To improve the purity of coal and prevent damage to the coal mining machine, it is necessary to identify coal and rock in underground coal mines. At the same time, the mined coal needs to be purified to remove rock and gangue. These two procedures are manually operated by workers in most coal mines. The realization of automatic identification and purification is not only conducive to the automation of coal mines, but also ensures the safety of workers. We discuss the possibility of using image-based methods to distinguish them. In order to find a solution that can be used in both scenarios, a …

SNAT-YOLO：面向边缘的矸石检测高效跨层聚合网络

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

本文提出改进YOLOv11的轻量化SNAT-YOLO，通过ShuffleNetV2、ADown、C2PSA-TriAtt和Inner-FocalerIoU实现煤矸石高精度低耗边缘检测。

结合自注意力与空洞卷积的煤显微组分语义分割

Combining Self-attention and Dilation Convolutional for Semantic Segmentation of Coal Maceral Groups

针对现有煤显微组分语义分割模型参数多、训练样本获取难的问题，提出基于IoT的DA-VIT并行网络，通过解耦并行与骨干网络、引入DCSA机制降参提效，在多项指标上优于现有最优方法。

Identification of Binary Neutron Star Mergers in Gravitational-Wave Data Using YOLO One-Shot Object Detection

We demonstrate the application of the YOLOv5 model, a general purpose convolution-based single-shot object detection model, in the task of detecting binary neutron star (BNS) coalescence events from gravitational-wave data of current generation interferometer detectors. We also present a thorough explanation of the synthetic data generation and preparation tasks based on approximant waveform models used for the model training, validation and testing steps. Using this approach, we achieve mean average precision ($\text{mAP}_{[0.50]}$) values of 0.945 for a single class validation dataset and as…

How Convolutional Neural Network Architecture Biases Learned Opponency and Colour Tuning

Recent work suggests that changing Convolutional Neural Network (CNN) architecture by introducing a bottleneck in the second layer can yield changes in learned function. To understand this relationship fully requires a way of quantitatively comparing trained networks. The fields of electrophysiology and psychophysics have developed a wealth of methods for characterising visual systems which permit such comparisons. Inspired by these methods, we propose an approach to obtaining spatial and colour tuning curves for convolutional neurons, which can be used to classify cells in terms of their spat…