关键词: 目标检测, 属性识别, 深度学习, 卷积神经网络, 图像识别

Abstract:

In order to improve image object recognition speed for multi-attributes of multi-values, an end-to-end recognition algorithm is proposed. Firstly, the modified YoloV3 network is used as main network in order to detect the object bounding boxes. Sub-networks are constructed according to the independent attributes. Sub-networks share the deep bounding box features of main network and adopt multi-outputs to recognize the attributes multi-values. There are three stages with different objective functions in the training process. Experimental results show that the proposed algorithm has good performance.

Key words: object detection, attribute recognition, deep learning, convolutional neural network, image recognition