This paper utilizes three popular semantic segmentation networks, specifically DeepLab v3+, fully convolutional network (FCN), and U-Net to qualitively analyze and identify the key components of cutting slope images in complex scenes and achieve rapid image-based slope detection. The elements of cutting slope images are divided into 7 categories. In order to determine the best algorithm for pixel level classification of cutting slope images, the networks are compared from three aspects: a) different neural networks, b) different feature extractors, and c) 2 different optimization algorithms. It is found that DeepLab v3+ with Resnet18 and Sgdm performs best, FCN 32s with Sgdm takes the second, and U-Net with Adam ranks third. This paper also analyzes the segmentation strategies of the three networks in terms of feature map visualization. Results show that the contour generated by DeepLab v3+ (combined with Resnet18 and Sgdm) is closest to the ground truth, while the resulting contour of U-Net (combined with Adam) is closest to the input images.

本文利用三种流行的语义分割网络，特别是 DeepLab v3+、完全卷积网络（FCN）和 U-Net，对复杂场景中切割边坡图像的关键成分进行定性分析和识别，实现基于图像的快速边坡检测。切割边坡图像的元素分为 7 类。为了确定切割边坡图像像素级分类的最佳算法，从三个方面对网络进行了比较：a）不同的神经网络，b）不同的特征提取器，c）2种不同的优化算法。发现采用 Resnet18 和 Sgdm 的 DeepLab v3+ 表现最好，采用 Sgdm 的 FCN 32s 次之，采用 Adam 的 U-Net 排名第三。本文还从特征图可视化方面分析了三个网络的分割策略。