Vision-based Identification Tracking of Steel Products via Depth Image-based Feature Extraction

10.29428/9789860544169.201801.0084

Li-Wei Kang；Hsin-Yi Lin；Ru-Hong Fu；Wei-Chen Jhong；I-Chen Tung；Chao-Yung Hsu

smart manufacturing ； Industry 4.0 ； steel industry ； identification tracking ； depth image

NCS 2017 全國計算機會議

2017（2018 / 01 / 01）

439 - 442

英文

To achieve smart manufacturing in Industry 4.0 for steel industry, this paper considers a smart steel manufacturing framework, where an automatic identification tracking method for steel products is developed. Automatically online tracking and identifying steel products on a production line is essential for smart manufacturing management since those products might be frequently moved and processed everywhere on the product flow. Existing approaches usually rely on marking or embedding a series of identification codes on the steel surfaces. However, steelmaking is usually processed under a very high temperature environment, making it difficult to well embed the identification codes with acceptable quality for further automatically recognizing them online. To tackle this problem, this paper proposes a vision-based automatic identification tracking method without needing to embed any identification codes onto the steel product surfaces. The key is to employ the essential identity of a steel product without extrinsic information embedded, achieved by extracting visual features from the steel image. Here, we capture the depth image for each steel product and extract its depth-based features. Our preliminary results have verified the efficiency of the proposed method.