关键词: 语义分割, 卷积神经网络, 图像分割, 无人驾驶

Abstract:

Image semantic segmentation is one of the important components of computer vision perception system. Aiming at the problem of slow segmentation speed of existing semantic segmentation algorithms, an improved real-time image semantic segmentation algorithm based on DeepLabv2 is proposed. Compared with DeepLabv2, this algorithm uses light-weight convolution neural network which is Xception as the encoder, adds the decode process by the Feature Pyramid Net （FPN）, reduces the number of parameters of Atrous convolution Spatial Pyramid Pooling （ASPP）, so that greatly compresses the algorithm model and improves the algorithm’s segmentation speed. In addition, this paper improves the problem that the Focal Loss function is difficult to select hyper-parameters in multi-classification tasks and applies it to the algorithm in this paper to improve the segmentation accuracy of the algorithm. The experimental results on Cityscapes and Pascal VOC2012 show that the proposed algorithm can achieve real-time segmentation speed and has the advantage of high segmentation accuracy. Meanwhile, it also shows that the proposed hyper-parameter selection method can further improve the segmentation accuracy of the algorithm.

Key words: semantic segmentation, Convolution Neural Network（CNN）, image segmentation, unmanned