Reliable multitype and orientation vessel detection is of vital importance for maritime surveillance. We develop three separate convolutional neural network (CNN) models for high-performance single-class vessel detection and then multiclass vessel-type/orientation detection. We also propose a modular combined network, which enhances the multiclass operation. The initial three models provide reliable F1 scores of 85%, 82%, and 76%, respectively. In addition, the modular combined approach improves the F1 scores for the multitype and orientation vessel detection by 2% and 3%, respectively. The training and testing were done on a dataset, including the multitype/orientation annotations, covering 31,078 vessel labels (10 vessel types and 5 orientations), which is offered to public access.
KEYWORDS: Data modeling, Performance modeling, Statistical modeling, Sensors, Visual process modeling, Surveillance, Maritime surveillance, Video surveillance, Visualization, Surveillance systems
This paper proposes a novel self-learning framework, which converts a noisy, pre-labeled multi-class object dataset into a purified multi-class object dataset with object bounding-box annotations, by iteratively removing noise samples from the low-quality dataset, which may contain a high level of inter-class noise samples. The framework iteratively purifies the noisy training datasets for each class and updates the classification model for multiple classes. The procedure starts with a generic single-class object model which changes to a multi-class model in an iterative procedure of which the F-1 score is evaluated to reach a sufficiently high score. The proposed framework is based on learning the used models with CNNs. As a result, we obtain a purified multi-class dataset and as a spin-off, the updated multi-class object model. The proposed framework is evaluated on maritime surveillance, where vessels need to be classified into eight different types. The experimental results on the evaluation dataset show that the proposed framework improves the F-1 score approximately by 5% and 25% at the end of the third iteration, while the initial training datasets contain 40% and 60% inter-class noise samples (erroneously classified labels of vessels and without annotations), respectively. Additionally, the recall rate increases nearly by 38% (for the more challenging 60% inter-class noise case), while the mean Average Precision (mAP) rate remains stable.
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