Development of computer vision informed container crane operator alarm methods

To reduce the extra work, the operation cost, and the risk of cargo delay induced by the unloading of wrong containers, this study first develops a container color detection model to predict the color of the container being unloaded. The prediction results are then used to develop two crane operator alarm methods. Method 1 alerts the crane operator if the detected color of a container is not in compliance with the correct container color. Method 2 constructs a decision problem to decide whether to alert the operator. The results of numerical experiments show that methods 1 and 2 are better than the benchmark. Specifically, method 1 can save the expected annual total cost by about 82% while method 2 can save the expected annual total cost by about 85%. Extensive sensitivity analysis is also conducted to verify the methods performance and robustness.

• Record URL:
  • https://doi.org/10.1080/23249935.2022.2145862
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23249935
• Supplemental Notes:
  • © 2024 Hong Kong Society for Transportation Studies Limited. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Yan, Ran
  • Tian, Xuecheng
  • Wang, Shuaian
  • Peng, Chuansheng
• Publication Date: 2024-5

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: 2145862
• Serial:
  • Transportmetrica A: Transport Science
  • Volume: 20
  • Issue Number: 2
  • Publisher: Taylor & Francis
  • ISSN: 2324-9935
  • EISSN: 2324-9943
  • Serial URL: http://www.tandfonline.com/loi/ttra21

Subject/Index Terms

• TRT Terms: Alarm systems; Computer vision; Container handling; Cranes; Detection and identification; Operating costs
• Subject Areas: Data and Information Technology; Finance; Freight Transportation; Terminals and Facilities;

Filing Info

• Accession Number: 01909859
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 26 2024 3:42PM