A benchmark image dataset for industrial tools

Luo, Cai and Yu, Leijian and Yang, Erfu and Zhou, Huiyu and Ren, Peng (2019) A benchmark image dataset for industrial tools. Pattern Recognition Letters, 125. pp. 341-348. ISSN 0167-8655

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    Abstract

    Robots and Artificial Intelligence (AI) play an increasingly important role in manufacture. One of the tasks is to identify tools in the scene so that the tools can be applied to different assembly purposes. In the AI community, many datasets have been generated and deployed to train robots to recognize individual items, however, these datasets are scene-specific and lack generic background. In this paper, we report our dataset contains photos of 8 objects types that would be easily recognized by qualified workers. This is achieved by gathering images of common tools in a typical factory. The ground truth categories of our dataset are manually labeled by experienced workers, which would be worthy evaluation tools for the intelligence industrial systems. The equipment used and the image collection process are discussed, along with the data format. The mean average precisions range from 64.37% to 78.20%, which bring the possibility for future improvement. The dataset is ideal to evaluate and benchmark view-point variant, vision-based control algorithm for industry robots. It is now public available from

    Author(s):Luo, Cai, Yu, Leijian, Yang, Erfu ORCID logoORCID: https://orcid.org/0000-0003-1813-5950, Zhou, Huiyu and Ren, Peng
    Item type:Article
    ID code:68402
    Keywords:benchmark, industrial tools, image dataset, Engineering design, Artificial Intelligence, Signal Processing, Software, Computer Vision and Pattern Recognition
    Subjects:Technology > Engineering (General). Civil engineering (General) > Engineering design
    Department:Faculty of Engineering > Design, Manufacture and Engineering Management
    Depositing user: Pure Administrator
    Date deposited:14 Jun 2019 09:44
    Last modified:16 Nov 2019 02:01
    URI:https://strathprints.strath.ac.uk/id/eprint/68402
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