Maritime ship detection plays a crucial role in smart ships and intelligent transportation systems. However, adverse maritime weather conditions, such as rain streak and fog, can significantly impair the performance of visual systems for maritime traffic. These factors constrain the performance of traffic monitoring systems and ship-detection algorithms for autonomous ship navigation, affecting maritime safety. The paper proposes an approach to resolve the problem by visually removing rain streaks and fog from images, achieving an integrated framework for accurate ship detection. Firstly, the paper employs an attention generation network within an adversarial neural network to focus on the distorted regions of the degraded images. The paper also utilizes a contextual encoder to infer contextual information within the distorted regions, enhancing the credibility of image restoration. Secondly, a weighted bidirectional feature pyramid network (BiFPN) is introduced to achieve rapid multi-scale feature fusion, enhancing the accuracy of maritime ship detection. The proposed GYB framework was validated using the SeaShip dataset. The experimental results show that the proposed framework achieves an average accuracy of 96.3%, a recall of 95.35%, and a harmonic mean of 95.85% in detecting maritime traffic ships under rain-streak and foggy-weather conditions. Moreover, the framework outperforms state-of-the-art ship detection methods in such challenging weather scenarios.
Automatic Identification System (AIS) data-supported ship trajectory analysis consistently helps maritime regulations and practitioners make reasonable traffic controlling and management decisions. Significant attentions are paid to obtain an accurate ship trajectory by learning data feature patterns in a feedforward manner. A ship may change her moving status to avoid potential traffic accident in inland waterways, and thus, the ship trajectory variation pattern may differ from previous data samples. The study proposes a novel ship trajectory exploitation and prediction framework with the help of the bidirectional long short-term memory (LSTM) (Bi-LSTM) model, which extracts intrinsic ship trajectory features with feedforward and backward manners. We have evaluated the proposed ship trajectory performance with single and multiple ship scenarios. The indicators of mean absolute error (MAE), mean absolute percentage error (MAPE) and mean square error (MSE) suggest that the proposed Bi-LSTM model can obtained satisfied ship trajectory prediction performance.
Large-scale deployed cameras in the automated container terminal (ACT) area helps on-site staff better identify unexpected yet emergency events by monitoring port personnel trajectories. Rainy weather is a common yet typical problem which may significantly deteriorate trajectory extraction performance. To tackle the problem, the study proposes an ensemble framework to extract personnel trajectory from port-like surveillance videos under varied rainy weather scenarios. Firstly, the proposed framework learns fine-grained personnel features with the help of the object query and transformer encoder-decoder module from the input port-like image sequences, and thus obtains port personnel locations from the input low-visibility images. Secondly, the personnel positions are further associated in a frame-by-frame manner with the help of neighboring kinematic movement information and feature information. Finally, a memory mechanism is introduced in the proposed framework to suppress personnel trajectory discontinuity outlier. In that manner, we can obtain accurate yet consistent personnel trajectories, and each person is assigned with a unique ID. We verified the proposed model performance on three port-like rainy videos involving with interferences of rain, rain streak and fog. Experimental results show that the proposed port personnel trajectory extraction framework can obtain satisfied performance considering that the average multi-target accuracy (MOTA), the average value of judging the same target ( ${\mathbf{IDF}}_{\mathbf{1}}$ ), average recall rate (IDR) and average precision (IDP) were larger than 92%.
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