A Semi-Supervised Semantic Segmentation Method for Blast-Hole Detection

Abstract

The goal of blast-hole detection is to help place charge explosives into blast-holes. This process is full of challenges, because it requires the ability to extract sample features in complex environments, and to detect a wide variety of blast-holes. Detection techniques based on deep learning with RGB-D semantic segmentation have emerged in recent years of research and achieved good results. However, implementing semantic segmentation based on deep learning usually requires a large amount of labeled data, which creates a large burden on the production of the dataset. To address the dilemma that there is very little training data available for explosive charging equipment to detect blast-holes, this paper extends the core idea of semi-supervised learning to RGB-D semantic segmentation, and devises an ERF-AC-PSPNet model based on a symmetric encoder–decoder structure. The model adds a residual connection layer and a dilated convolution layer for down-sampling, followed by an attention complementary module to acquire the feature maps, and uses a pyramid scene parsing network to achieve hole segmentation during decoding. A new semi-supervised learning method, based on pseudo-labeling and self-training, is proposed, to train the model for intelligent detection of blast-holes. The designed pseudo-labeling is based on the HOG algorithm and depth data, and proved to have good results in experiments. To verify the validity of the method, we carried out experiments on the images of blast-holes collected at a mine site. Compared to the previous segmentation methods, our method is less dependent on the labeled data and achieved IoU of 0.810, 0.867, 0.923, and 0.945, at labeling ratios of 1/8, 1/4, 1/2, and 1.