关键词: 自适应边距损失, 卷积神经网络, 语义分割, 车辆外观数据集

Abstract: Vehicle appearance segmentation is a major application of computer vision in the traffic scenes. In recent years, due to the fast development of deep learning technique, CNN based image segmentation methods have obtained great breakthrough. In order to solve the data imbalance problem, this paper proposes a new adaptive margin loss function to replace softmax loss. A CNN model is trained to achieve end-to-end pixel level semantic segmentation. Besides, a vehicle appearance dataset is built for model training and testing. Experimental results demonstrate that the new method shows superior performance, especially for categories with small area.

Key words: adaptive margin loss, convolutional neural network, semantic segmentation, vehicle appearance dataset