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煤炭作为我国的基础能源和关键原料，其重要性毋庸置疑，在推动经济社会的健康快速发展中扮演了不可或缺的角色。在煤炭的生产过程中，煤矸石的有效分选成为了确保原煤质量的关键环节。然而，由于传统的煤矸石分选方法效率低下、准确率不高，已经难以满足现代化工业生产的需求。

得益于深度学习技术近年来在计算机视觉领域的突破，本文探索其在煤矸石智能分选环节中的新型应用。与传统人工分选相比，深度学习目标检测技术以高效率、高准确性和易操作性的优势为煤矸石分选中煤矸石的识别和定位提供了创新解决方案。本研究旨在通过引入基于卷积神经网络的目标检测算法，快速识别并定位煤炭与煤矸石的位置，减少传统分选法引起的误判，提升分选速度，同时降低资源消耗，对于煤炭行业的环保节能起到积极作用。

针对真实的现场分选环境中，存在的煤矸石的尺度变化较大，物料相互遮挡重叠，环境干扰因素较大，及背景容易混淆的问题，提出一种基于YOLOv8改进的煤矸石目标检测算法---YOLO-Coal，通过引入DCN（可变形卷积网络）和CBAM（卷积块注意力模块），对特征图中的不规则形状进行更好地特征提取，使骨干网络更好地适应不规则的空间结构，更精准地关注重要目标，从而提高模型对目标的检测能力。同时引入基于辅助边框的Inner-IoU损失函数，加速模型收敛和提高检测精度。在收集到的煤矸石数据集上，进行实验分析，识别的精确度获得显著提升。YOLO-Coal模型对煤炭识别的准确率和召回率分别达到了98.6\%和98.9\%，相较于基线模型，分别提升了1.5和3个百分点。结果表明，YOLO-Coal模型有效地实现了在复杂工况环境下对煤炭的准确识别与定位，能高效地进行煤矸石分选。

在此基础上依托智能煤矸石分选系统，进行了模型的推理优化部署。通过设计实验并验证，证明了研究的YOLO-Coal算法能够准确高效地检测煤矸石。这一算法使得整个智能分选系统方法简单易用，研究表明，YOLO-Coal算法具有较高的识别精度，同时其执行反应速度与检测识别速度均能满足实时检测的要求。

总结来说，本文的工作不仅展现了深度学习目标检测技术在煤矸石智能分选领域的应用潜力，还通过构建大型煤矸石数据集、优化目标检测算法和实现智能分选系统的设计与部署，为煤炭行业的环保节能和资源高效利用提供了有力的技术支撑。

Coal, as the fundamental energy source and vital raw material in China, undoubtedly plays an essential role in driving healthy and rapid economic and social development. In the production process of coal, the effective separation of coal gangue has become a critical step in ensuring the quality of raw coal. However, traditional methods of coal gangue separation are inefficient and lack accuracy, making it increasingly challenging to meet the demands of modern industrial production.

Benefiting from recent breakthroughs in deep learning technology in the field of computer vision, this paper explores its novel application in the intelligent separation of coal gangue. In comparison to traditional manual separation, deep learning object detection technology provides an innovative solution for the identification and localization of coal gangue in coal gangue separation, offering advantages in efficiency, accuracy, and ease of operation. This study aims to rapidly identify and locate the positions of coal and coal gangue by introducing a convolutional neural network-based object detection algorithm, reducing misjudgments caused by traditional separation methods, improving separation speed, and reducing resource consumption, thereby positively contributing to environmental protection and energy conservation in the coal industry.

Addressing challenges in real-world separation environments, such as significant scale variations of coal gangue, material overlap and occlusion, significant environmental interference, and easily confused backgrounds, a YOLOv8-based improved coal gangue detection algorithm named YOLO-Coal is proposed. By introducing Deformable Convolutional Networks (DCN) and Convolutional Block Attention Modules (CBAM), irregular shapes in feature maps are better extracted, enabling the backbone network to adapt better to irregular spatial structures and focus more accurately on important targets, thereby enhancing the model's detection capabilities. Simultaneously, the Inner-IoU loss function based on auxiliary bounding boxes is introduced to accelerate model convergence and improve detection accuracy. Experimental analysis on a collected coal gangue dataset demonstrates a significant improvement in detection accuracy. The YOLO-Coal model achieves coal recognition accuracy and recall rates of 98.6% and 98.9%, respectively, representing increases of 1.5 and 3 percentage points compared to the baseline model. Results indicate that the YOLO-Coal model effectively achieves accurate identification and localization of coal in complex operating environments, enabling efficient coal gangue separation.

Building upon this, the intelligent coal gangue separation system is deployed with model inference optimization. Experimental design and validation demonstrate that the YOLO-Coal algorithm can accurately and efficiently detect coal gangue, simplifying the entire intelligent separation system, facilitating portability, exhibiting high recognition accuracy, and meeting real-time detection requirements for both execution reaction speed and detection recognition speed.

In conclusion, this work not only demonstrates the potential application of deep learning object detection technology in the field of intelligent coal gangue separation but also provides robust technical support for environmental protection, energy conservation, and efficient resource utilization in the coal industry through the construction of a large-scale coal gangue dataset, optimization of object detection algorithms, and implementation of intelligent separation system design and deployment.