Incremental Learning-based Lane Detection for Automated Rubber-Tired Gantries in Container Terminal

Yunjian Feng, Kunyang Zhou, Jun Li, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review


Lane detection, one of the crucial foundations of the autonomous driving of Rubber-Tired Gantries (RTGs), plays a vital role in automating manual container terminals. Deep-learning-based lane detection methods have robust and generalized global feature extraction capabilities to deal with complex scenarios well. However, the high preparation cost of large-scale labeled data has limited their application in RTG lane detection. Therefore, this paper presents a cost-effective, scalable incremental learning-based detection method. Specifically, some lane images are collected online, with reliable segmentation labels generated by an image-processing-based lane detection method. Next, a semi-supervised clustering approach is employed to construct a dynamically expanding sample pool, ensuring that samples are representative and diverse. Finally, a lane detection network model is self-trained by using all labeled and unlabeled samples. Extensive experimental results show that our proposed method outperforms existing methods and can achieve a lane detection accuracy of 94.87% and a detection success rate of 99.06%, with the potential for further performance improvement as data size increases..

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Circuits and Systems for Video Technology
StateAccepted/In press - 2023

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Electrical and Electronic Engineering


  • Adaptation models
  • Autonomous vehicles
  • Container terminal
  • Containers
  • Contrastive learning
  • Feature extraction
  • Incremental learning
  • Lane detection
  • Lane detection
  • Rubber-Tired Gantry
  • Task analysis
  • Training


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