Abstract
Container terminal automation offers many potential benefits, such as increased productivity, reduced cost, and improved safety. Autonomous trucks can lead to more efficient container transport. A novel lane detection method is proposed using score-based generative modeling through stochastic differential equations for image-to-image translation. Image processing techniques are combined with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Genetic Algorithm (GA) to ensure fast and accurate lane positioning. A robust lane detection method can deal with complicated detection problems in realistic road scenarios. The proposed method is validated by a dataset collected from the port terminals under different environmental conditions; in addition, the robustness of the lane detection method with stochastic noise is tested.
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