Research On Deep Learning Coal Gangue Detection Algorithm And Embedded Platform Implementation

Coal gangue separation is a necessary link to ensure the efficient utilization of coal in the process of coal mining,and the realization of automatic separation of coal gangue is an iridispensable technology for the construction of smart mines.With the development of image processing technology and deep learning technology,object detection algorithms based on deep learning have been applied in coal gangue separation scenarios and have achieved remarkable results.The paper proposes an anchor-free deep learning coal gangue detection algorithm based on YOLOv5 and designs an embedded platform deployment scheme for coal gangue separation scenarios.The paper adopts a variety of improvement strategies to effectively improve the performance of the coal gangue detection algorithm.Aiming at the problem of an unbalanced distribution of positive and negative samples in the YOLOv5 object detection algorithm,the anchor-free strategy is used to complete the box regression task,and the sample allocation strategy and joint loss function are updated at the same time.Aiming at the problem that coal gangue is difficult to be accurately detected due to the influence of the environment,the CA attention mechanism is introduced into the YOLOvS network structure to enhance the saliency of the object in the complex background and improve the expression ability of the feature.The YOLOv5 object detection algorithm uses a shared convolutional layer in the model detection head to complete the classification and regression tasks.The inconsistency of the two tasks in the spatial dimension limits the detection performance,The shared convolutional layer is decoupled to generate two convolutional branch structures respectively.Completing the classifcation and regression tasks in different spatial dimensions avoids inconsistency issues.The improved coal gangue detection model achieves AP50-95 of 74.9%,70.4%,and 82.6%on coal gangue datasets in three different regions,which are 3.0%,4.7%,and 3.6%higher than the original YOLOv5 algorithm.The experimental results show that the anchor-free deep learning coal gangue detection algorithm based on YOLOv5 can effectively improve the coal gangue detection performance.Given the problems of low computing power and power consumption limitations of embedded platforms,the paper makes a lightweight improvement on the network structure of the anchor-free deep learning coal gangue detection model.The model is lightweight by redesigning the backbone network,and TensorRT is used to optimize the model structure,reduce the numerical accuracy of model parameters,and improve the resource utilization of the embedded platform.At the same time,a coal gangue separation system is built.On the coal gangue datasets in three different regions,the detection accuracy AP50-95 of the optimized model reaches 72.2%,68.9%,and 79.2%,and the detection rate on the embedded platform reaches 52FPS.The accuracy rates in the coal gangue separation experiments were all above 95.0%.The experimental results show that the lightweight coal gangue detection model can effectively detect coal gangue and the detection performance is better than the current excellent lightweight object detection models,which can meet the needs of coal gangue separation application scenarios.