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

Engineering notes

Extensive experimental results show that our proposed method outperforms existing methods and achieves 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.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

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 achieves 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.

Links and sources

• Official / arXiv page

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