Deep Learning Based Granularity Detection Network for Mine Dump Materials

Abstract

The granularity distribution of mine dump materials has received extensive attention as an essential research basis for dump stability and mine land reclamation. Image analysis is widely used as the fastest and most efficient method to obtain the granularity distribution of the dump materials. This article proposes a deep learning-based approach for granularity detection and identification of mine dump material, conglomerate, and clay. Firstly, a Conglomerate and Clay Dataset (CCD) is proposed to study the granularity of the mine dump. A typical study area is selected for field sampling, and the sampled conglomerate and clay is photographed and labeled. In addition, this article proposes a keypoint-based detection algorithm for the conglomerate and clay detection. The algorithm considers the scale variation of conglomerate and clay in orthophoto images and adopts center point detection to avoid the difficulty of localization. On this basis, dense convolution is introduced in feature extraction to reduce the computational redundancy to conduct detection more efficiently. Finally, the corresponding granularity distributions of conglomerate and clay are obtained by geometric calculation in the deep learning-based detection results. The proposed algorithm is validated on the proposed dataset CCD, and the experiments demonstrate the effectiveness of the proposed algorithm and its application to the granularity analysis of mine dump material.