关键词: 外观质量检测, 目标检测, 云边协同, 公开数据集

Abstract: Traditional appearance quality detection methods need manual intervention,and their detection accuracy and automation level are very low,which has become a serious bottleneck hindering the acceleration of production rhythm and the improvement of work efficiency.To fully tap the potential value of industrial data,an appearance quality detection image dataset was collated and annotated,which had been published as an open dataset named with SDU-Haier-AQD on the IEEE Data Port website.Based on the open dataset,a Fast Tiny Yolo model (FT-Yolo)was proposed to conduct the appearance quality detection tasks,which achieved more than 97% recognition accuracy in the detection tasks of 11 types of air conditioners and 16 categories of appearance quality features.The detection time could be reduced by more than 90%,and the automatic detection and accurate and fast recognition of appearance quality features were realized.An intelligent system for product appearance quality detection based on cloud-edge collaboration mechanism was developed,which could interact with the production lines and cloud platform efficiently,and realize the real-time collection,transmission,intelligent detection and cloud storage and management of the appearance quality data.

Key words: appearance quality detection, object detection, cloud-edge collaboration, open datasets