School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Shaanxi Provincial Key Laboratory of Intelligent Testing of Mine Mechanical and Electrical Equipment, Xi'an 710054, China.
Sensors (Basel). 2022 Dec 29;23(1):374. doi: 10.3390/s23010374.
Research on coal foreign object detection based on deep learning is of great significance to safe, efficient, and green production of coal mines. However, the foreign object image dataset is scarce due to collection conditions, which brings an enormous challenge to coal foreign object detection. To achieve augmentation of foreign object datasets, a high-quality coal foreign object image generation method based on improved StyleGAN is proposed. Firstly, the dual self-attention module is introduced into the generator to strengthen the long-distance dependence of features between spatial and channel, refine the details of the generated images, accurately distinguish the front background information, and improve the quality of the generated images. Secondly, the depthwise separable convolution is introduced into the discriminator to solve the problem of low efficiency caused by the large number of parameters of multi-stage convolutional networks, to realize the lightweight model, and to accelerate the training speed. Experimental results show that the improved model has significant advantages over several classical GANS and original StyleGAN in terms of quality and diversity of the generated images, with an average improvement of 2.52 in IS and a decrease of 5.80 in FID for each category. As for the model complexity, the parameters and training time of the improved model are reduced to 44.6% and 58.8% of the original model without affecting the generated images quality. Finally, the results of applying different data augmentation methods to the foreign object detection task show that our image generation method is more effective than the traditional methods, and that, under the optimal conditions, it improves AP by 5.8% and AP by 4.5%.
基于深度学习的煤炭异物检测研究对煤矿的安全、高效、绿色生产具有重要意义。然而，由于采集条件的限制，异物图像数据集稀缺，这给煤炭异物检测带来了巨大的挑战。为了实现异物数据集的扩充，提出了一种基于改进 StyleGAN 的高质量煤炭异物图像生成方法。首先，在生成器中引入双自注意力模块，增强特征在空间和通道之间的长距离依赖关系，细化生成图像的细节，准确区分前后背景信息，提高生成图像的质量。其次，在鉴别器中引入深度可分离卷积，解决多阶段卷积网络参数较多导致的效率低下问题，实现轻量化模型，加快训练速度。实验结果表明，改进后的模型在生成图像的质量和多样性方面与几个经典的 GANs 和原始 StyleGAN 相比具有显著优势，每个类别的 IS 平均提高了 2.52，FID 降低了 5.80。就模型复杂度而言，改进后的模型的参数和训练时间分别减少到原始模型的 44.6%和 58.8%，而不影响生成图像的质量。最后，将不同的数据增强方法应用于异物检测任务的结果表明，我们的图像生成方法比传统方法更有效，在最优条件下，AP 提高了 5.8%，AP 提高了 4.5%。
