A synchronous detection-segmentation method for oversized gangue on a coal preparation plant based on multi-task learning

Abstract

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.