Abstract
This paper presents Deep Networks for Improved Segmentation Edges (DeNISE), a novel data enhancement technique using edge detection and segmentation models to improve the boundary quality of segmentation masks. DeNISE utilizes the inherent differences in two sequential deep neural architectures to improve the accuracy of the predicted segmentation edge. DeNISE applies to all types of neural networks and is not trained end-to-end, allowing rapid experiments to discover which models complement each other. We test and apply DeNISE for building segmentation in aerial images. Aerial images are known for difficult conditions as they have a low resolution with optical noise, such as reflections, shadows, and visual obstructions. Overall the paper demonstrates the potential for DeNISE. Using the technique, we improve the baseline results with a building IoU of 78.9%.
Abstract (translated)
本论文介绍了改进分割边缘的深度学习方法(DeNISE),一种利用边缘检测和分割模型提高分割掩膜边界质量的全新的数据增强技术。DeNISE利用两个连续深度学习架构之间的固有差异来提高预测分割边缘的精度。DeNISE适用于各种类型的神经网络,不需要整个网络进行训练,因此能够迅速实验以确定哪些模型互相补充。我们测试和应用了DeNISE在航空图像中进行分割。航空图像因具有低分辨率和光学噪声(如反射、阴影和视觉障碍)而著称,难以处理。总体而言,本文展示了DeNISE的潜力。利用该技术,我们取得了78.9%的建移IoU提高。
URL
https://arxiv.org/abs/2309.02091
