https://github.com/smilell/AG-CNN Experimental results are shown in Tab. 12, 13, 14, 15 and 16.“A” in the tables means balanced accuracy,“E” is the widely used overlapping error, and δ denotes absolute CDR error. Table 12. Summary of several results for OD/OC segmentation on Dr...
lstm-rnn, seq2seq model and attention-seq2seq model for vessel trajectory prediction. lstm-modelattention-mechanismseq2seq-modelvesseltrajectory-predictiontensorflow2 UpdatedApr 20, 2021 Python Deep Vessel Segmentation by Learning Graphical Connectivity ...
If available Link to developer documentation/manual https://github.com/PilotLeaf/PyVT/tree/main/userguide Support email for questions leaf-pilot@outlook.com 1. Motivation and significance Vessel spatio-temporal trajectory data mainly refers to the ship Automatic Identification System (AIS) data. AIS...
Vessel trajectory prediction technology plays a crucial role in route optimization and collision avoidance. However, current prediction methods face limitations when dealing with complex vessel interactions and multi-dimensional attribute information. Most models rely solely on global modeling in the temporal...
The XGBoost model tuned using boosted PSO attained an overall accuracy of 99.72% for the vessel classification problem, while the LSTM model attained a mean square error (MSE) of 0.000098 for the marine trajectory prediction challenge. A rigid statistical analysis of the classification model was ...
Methods of navigation based on vision, LiDAR, and inertial sensors to maintain accurate position and trajectory need to be developed for situations where GNSS signals can not be used. The future integration of X-band radar data for object detection in adverse conditions, such as darkness and fog...
Methods of navigation based on vision, LiDAR, and inertial sensors to maintain accurate position and trajectory need to be developed for situations where GNSS signals can not be used. The future integration of X-band radar data for object detection in adverse conditions, such as darkness and fog...