Wang Niannian, Zhang Jingzheng, Song Xiaotian
School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China.
School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China.
Sensors (Basel). 2023 Nov 7;23(22):9019. doi: 10.3390/s23229019.
Deep learning algorithms have achieved encouraging results for pipeline defect segmentation. However, existing defect segmentation methods may encounter challenges in accurately segmenting the complex features of pipeline defects and suffer from low processing speeds. Therefore, in this study, we propose Pipe-Sparse-Net, a pipeline defect segmentation system that combines StyleGAN3 to segment the complex forms of underground drainage pipe defects. First, we introduce a data augmentation algorithm based on StyleGAN3 to enlarge the dataset. Next, we propose Pipe-Sparse-Net, a pipeline segmentation model based on SparseInst, to accurately predict the defect regions in drainage pipes. Experimental results demonstrate that the segmentation accuracy of this model can reach 91.4% with a processing speed of 56.7 frames per second (FPS). To validate the superiority of this method, comparative experiments were conducted against Yolact, Condinst, and Mask R-CNN, and the model achieved a speed improvement of 45% while increasing the accuracy by more than 4%.
深度学习算法在管道缺陷分割方面取得了令人鼓舞的成果。然而，现有的缺陷分割方法在准确分割管道缺陷的复杂特征时可能会遇到挑战，并且处理速度较低。因此，在本研究中，我们提出了Pipe-Sparse-Net，这是一种结合StyleGAN3来分割地下排水管道缺陷复杂形式的管道缺陷分割系统。首先，我们引入了一种基于StyleGAN3的数据增强算法来扩大数据集。接下来，我们提出了基于SparseInst的管道分割模型Pipe-Sparse-Net，以准确预测排水管道中的缺陷区域。实验结果表明，该模型的分割准确率可达91.4%，处理速度为每秒56.7帧（FPS）。为了验证该方法的优越性，我们与Yolact、Condinst和Mask R-CNN进行了对比实验，该模型在提高准确率超过4%的同时，速度提升了45%。
