Enhancing coal-gangue object detection using GAN-based data augmentation strategy with dual attention mechanism

Coal separation based on computer vision has attracted substantial attention in recent years. However, developing reliable object detection models relies on large-scale annotated dataset, which in industrial practice is time-consuming and labor-intensive to obtain. In this paper, we propose a novel data augmentation model called dual attention deep convolutional generative adversarial network (DADCGAN) to expand dataset scale and improve object detection. For the first time, the proposed DADCGAN, which adopts DCGAN as its foundation architecture, introduces efficient channel attention and external attention mechanisms to capture essential feature information from the channel and spatial dimensions of images, respectively. Moreover, spectral normalization and two time-scale update rule strategies are incorporated to stabilize the training process. The implementation of our proposed data augmentation strategy includes two steps. First, traditional pixel transformation is used to expand an original small dataset. Then, our GAN-based data augmentation is executed to further expand the dataset by generating synthetic images. Experimental results show that our DADCGAN model achieves the lowest FID value, decreasing the FID by 21.30-71.96 % compared to other baseline GAN models, showcasing its ability to produce more realistic coal-gangue images. Finally, the data augmentation strategies are applied to the YOLOv4 model, enhancing the mAP by 9.26 %, highlighting its significance in enhancing coalgangue object detection. These results have important implications for the development and implementation of computer vision-based technologies, enabling the realization of cleaner and more efficient coal separation methods.