A data expansion strategy for improving coal-gangue detection
Luyao Wang, Xuewen Wang, Bo Li, Dailiang Wei
Abstract
With the development of machine vision and deep learning, the intelligent visual-based coal-gangue separation technology has gradually attracted the attention of enterprises and researchers. Coal-gangue detection models based on deep learning rely on a large number of CG11. CG: coal and gangue images for training, which is time-consuming and laborious to acquire. In this paper, based on small samples, image augmentation, and generative adversarial network were used to expand dataset of CG images to improve the performance of coal-gangue detectors. The dataset was expanded by pixel transform firstly. Next, four improved DCGAN structures were proposed to generate more diversiform CG images. Refer to the results of model training and generated image evaluation, DCGAN32 with three blocks had the best ability to generate more authentic image with the lowest FID value, scoring 93.8708 and 104.3394 on coal and gangue, respectively. As the training data expands, the performance of the object detection model was improved by up to 9.4% and 15.1%, respectively, on mAP and IoU. The proposed data expansion scheme can effectively improve the training performance of coal-gangue detectors based on small samples.