Online identification and sorting for coal and gangue has always been a hot issue in the field of coal processing intelligence. Existing research has focused on materials with particle sizes below 300 mm, and its front-end algorithms are dedicated to achieving image classification or object detection. The lack of detailed shape information of materials in these methods enables them to be not suitable for sorting oversized gangue. In this work, we proposed a synchronous detection-segmentation method for oversized gangue, which was implemented as a joint network based on the multi-task learning theory. The loss function of joint network and the feature interaction channels between the shared encoding module and the parallel decoding branches were designed to efficiently achieve object detection and semantic segmentation for oversized gangue. The proposed method has been evaluated in a comprehensive manner using huge amounts of coal-gangue images taken in an actual production process. The superiority of our joint network based on multi-task learning was verified by comparing several experimental results of them with the classical single-task networks. The issue of convergence synchronization between the multi-task branches was investigated to further optimize the segmentation results. Meanwhile, the effectiveness of the proposed method in improving the sorting capability of the manipulator was explained through a qualitative analysis for a case of sorting oversized gangue.

煤矸石在线识别分拣一直是煤炭加工智能化领域的热点问题。现有的研究主要集中在粒径在 300 mm 以下的材料上，其前端算法专门用于实现图像分类或物体检测。这些方法缺乏材料的详细形状信息，使其不适用于分选超大号脉石。在这项工作中，我们提出了一种基于多任务学习理论的联合网络实现超大矸石的同步检测-分割方法。设计了联合网络的损失函数以及共享编码模块和并行解码分支之间的特征交互通道，以有效实现超大矸石的目标检测和语义分割。使用在实际生产过程中拍摄的大量煤矸石图像对所提出的方法进行了综合评估。通过将它们的几个实验结果与经典的单任务网络进行比较，验证了我们基于多任务学习的联合网络的优越性。研究了多任务分支之间的收敛同步问题，以进一步优化分割结果。同时，通过对超大矸石分拣案例的定性分析，说明了该方法在提高机械手分拣能力方面的有效性。通过将它们的几个实验结果与经典的单任务网络进行比较，验证了我们基于多任务学习的联合网络的优越性。研究了多任务分支之间的收敛同步问题，以进一步优化分割结果。同时，通过对超大矸石分拣案例的定性分析，说明了该方法在提高机械手分拣能力方面的有效性。通过将它们的几个实验结果与经典的单任务网络进行比较，验证了我们基于多任务学习的联合网络的优越性。研究了多任务分支之间的收敛同步问题，以进一步优化分割结果。同时，通过对超大矸石分拣案例的定性分析，说明了该方法在提高机械手分拣能力方面的有效性。