YOLOv8-ER belt conveyor coal gangue target monitoring
[Objective]The primary objective of this study is to tackle the manifold challenges associated with identifying coal gangue during coal mining,including but not limited to the inefficiencies,high operational complexity,and substantial costs inherent in traditional identification methods.By integrating and refining advanced deep learning technologies,particularly the YOLOv8-based object detection model,we seek to develop an intelligent system capable of automatically,rapidly,and precisely identifying coal gangue.This intelligent system will not only enhance the efficiency and quality of coal mining and minimize human errors and cost inefficiencies but also foster the sustainable utilization of coal resources,mitigate environmental pollution,and provide technical support for the green transition of the coal industry.[Methods]To achieve the aforementioned research objectives,this study adopted a systematic research method.First,we conducted an in-depth analysis of the advantages and disadvantages of the YOLOv8 model and proposed the YOLOv8-ER model based on this.This model deeply optimized the backbone network of YOLOv8 by introducing an efficient channel attention network,enabling the model to focus more effectively on key features in the image and improve the accuracy and efficiency of feature extraction.At the same time,we applied structural reparameterization technology to simplify the network architecture of the model,reduce the computational complexity and resource consumption,and improve the inference speed and stability of the model.In the model training phase,we used a large-scale and diverse dataset of coal gangue images and continuously optimized the model parameters through multiple iterations of training to ensure the accuracy and robustness of the model in practical applications.[Results]This study has achieved multiple innovative results in the field of coal gangue identification.First,the YOLOv8-ER model has achieved significant improvements in both processing speed and detection accuracy.The high-speed detection capability of the YOLOv8-ER model(i.e.,240.8 frames/second)can meet the high real-time requirements of application scenarios,with a performance rate of up to 92.3％mAP@0.5,which ensures the accuracy and reliability of recognition.Second,compared with the traditional YOLOv8 model,YOLOv8-ER has made breakthroughs in multiple key indicators,such as an accuracy improvement of 3.2％,mAP@0.5 improved by 4.4％,and PR balance rate improved by 2.6％.These improvements make the model more competitive in practical applications.In addition,by optimizing the network structure and applying reparameterization techniques,we have successfully reduced the computational complexity and resource consumption of the model,improving its operational efficiency and economy.[Conclusions]In summary,this study successfully developed an efficient and accurate coal gangue identification technology by introducing and optimizing the YOLOv8-ER model.This technology not only overcomes the limitations of traditional recognition methods and improves the efficiency and quality of coal mining but also provides strong support for the intelligent transformation of the coal industry.In the future,with the continuous advancement of technology and the expansion of application scenarios,the YOLOv8-ER model is expected to play an important role in more fields,promoting the sustainable development and innovation of related industries.At the same time,we will continue to conduct in-depth research on the optimization and extended application of this model to meet the needs of coal gangue recognition in different scenarios.
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