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Deep learning–assisted real-time container corner casting recognition

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  • Jaecheul Lee

Abstract

Intelligent automated crane systems are now an integral part of container port automation. Accurate corner casting detection boosts the performance of an automated crane system which ultimately automates ships loading and unloading. Existing techniques use various traditional laser-based and vision-based methods for corner casting detection. Challenging weather conditions, varying lighting conditions, light reflections from ground, and container rusting conditions are the main problems that affect the performance of automated cranes. From this line of research, we propose an end-to-end method that takes a low-quality video input and produces bounding boxes around corner castings by applying a recurrent neural network along with long short-term memory units. The expressive image features from GoogLeNet are used to produce intermediate image representations that are further tuned for our system. The proposed system uses back-propagation to allow joint tuning of all components. At least, four cameras are mounted on each crane and input stream is combined into a single image to reduce the computational cost. The proposed system outperforms all existing methods in terms of precision, recall, and F -measure. The proposed method is implemented in a real-time port and produces more than 98% accuracy in all conditions.

Suggested Citation

  • Jaecheul Lee, 2019. "Deep learning–assisted real-time container corner casting recognition," International Journal of Distributed Sensor Networks, , vol. 15(1), pages 15501477188, January.
  • Handle: RePEc:sae:intdis:v:15:y:2019:i:1:p:1550147718824462
    DOI: 10.1177/1550147718824462
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    Cited by:

    1. Zuojin Li & Qing Yang & Shengfu Chen & Wei Zhou & Liukui Chen & Lei Song, 2019. "A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
    2. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).

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