面向云服务平台的产品感性评价及标注模型

To eliminate the differences of individual evaluation criteria in product Kansei attributes evaluation, and further improve the evaluation efficiency of product, an intelligent evaluation model based on YOLOv3 and DFL-CNN was proposed. The proposed model was oriented to cloud service platform of industrial design and divided into two steps: (1) YOLOv3 was used to locate the product and label its categories automatically; (2) the labeled images and its position coordinates were sent into DFL-CNN module to classify its Kansei attributes. A case study was provided to validate the proposed model. The proposed model transformed the design evaluation task into the recognition and classification task in the field of computer vision, and achieved over 95% accuracy in the binary and triple classification tasks. The elapsed time for this model on a GTX 1070 gpu was approximately 0.31 seconds. By comparing with other CNNs such as VGG-16, the validity and superiority of the proposed model were proved.