Knowledge-Driven Semantic Segmentation for Waterway Scene Perception

Semantic segmentation as one of the most popular scene perception techniques has been studied for autonomous vehicles. However, deep learning-based solutions rely on the volume and quality of data and knowledge from specific scene might not be incorporated. A novel knowledge-driven semantic segmentation method is proposed for waterway scene perception. Based on the knowledge that water is irregular and dynamically changing, a Life Time of Feature (LToF) detector is designed to distinguish water region from surrounding scene. Using a Bayesian framework, the detector as the likelihood function is combined with U-Net based semantic segmentation to achieve an optimized solution. Finally, two public datasets and typical semantic segmentation networks, FlowNet, DeepLab and DVSNet are selected to evaluate the proposed method. Also, the sensitivity of these methods and ours to dataset is discussed

GGT-YOLO: A Novel Object Detection Algorithm for Drone-Based Maritime Cruising

Drones play an important role in the development of remote sensing and intelligent surveillance. Due to limited onboard computational resources, drone-based object detection still faces challenges in actual applications. By studying the balance between detection accuracy and computational cost, we propose a novel object detection algorithm for drone cruising in large-scale maritime scenarios. Transformer is introduced to enhance the feature extraction part and is beneficial to small or occluded object detection. Meanwhile, the computational cost of the algorithm is reduced by replacing the convolution operations with simpler linear transformations. To illustrate the performance of the algorithm, a specialized dataset composed of thousands of images collected by drones in maritime scenarios is given, and quantitative and comparative experiments are conducted. By comparison with other derivatives, the detection precision of the algorithm is increased by 1.4%, the recall is increased by 2.6% and the average precision is increased by 1.9%, while the parameters and floating-point operations are reduced by 11.6% and 7.3%, respectively. These improvements are thought to contribute to the application of drones in maritime and other remote sensing fields

GGT-YOLO: A Novel Object Detection Algorithm for Drone-Based Maritime Cruising

Drones play an important role in the development of remote sensing and intelligent surveillance. Due to limited onboard computational resources, drone-based object detection still faces challenges in actual applications. By studying the balance between detection accuracy and computational cost, we propose a novel object detection algorithm for drone cruising in large-scale maritime scenarios. Transformer is introduced to enhance the feature extraction part and is beneficial to small or occluded object detection. Meanwhile, the computational cost of the algorithm is reduced by replacing the convolution operations with simpler linear transformations. To illustrate the performance of the algorithm, a specialized dataset composed of thousands of images collected by drones in maritime scenarios is given, and quantitative and comparative experiments are conducted. By comparison with other derivatives, the detection precision of the algorithm is increased by 1.4%, the recall is increased by 2.6% and the average precision is increased by 1.9%, while the parameters and floating-point operations are reduced by 11.6% and 7.3%, respectively. These improvements are thought to contribute to the application of drones in maritime and other remote sensing fields

Establishment of the Sustainable Ecosystem for the Regional Shipping Industry Based on System Dynamics

The rapid development of the shipping industry has brought great economic benefits but at a great environmental cost; exhaust emissions originating from ships are increasing, causing serious atmospheric pollution. Hence, the mitigation of ship exhaust emissions and the establishment of the sustainable ecosystem have become urgent tasks, which will require complicated and comprehensive systematic approaches to solve. We address this problem by establishing a System Dynamics (SD) model to help mitigate regional ship exhaust emissions without restricting economic growth and promote the development of the sustainable ecosystem. Factors correlated with ship exhaust emissions are identified, and a causal loop diagram is drawn to describe the complicated interrelations among the correlated factors. Then, a stock-and-flow diagram is designed and variable equations and parameter values are determined to quantitatively describe the dynamic relations among different elements. After verifying the effectiveness of the model, different scenarios for the sustainable development in the study area were set by changing the values of the controlling variables. The variation trends of the exhaust emissions and economic benefits for Qingdao port under different scenarios were predicted for the years 2015–2025. By comparing the simulation results, the effects of different sustainable development measures were analyzed, providing a reference for the promotion of the harmonious development of the regional environment and economy

Ship Intention Prediction at Intersections Based on Vision and Bayesian Framework

Due to the high error frequency of the existing methods in identifying a ship’s navigational intention, accidents frequently occur at intersections. Therefore, it is urgent to improve the ability to perceive ship intention at intersections. In this paper, we propose an algorithm based on the fusion of image sequence and radar information to identify the navigation intention of ships at intersections. Some existing algorithms generally use the Automatic Identification System (AIS) to identify ship intentions but ignore the problems of AIS delay and data loss, resulting in unsatisfactory effectiveness and accuracy of intention recognition. Firstly, to obtain the relationship between radar and image, a cooperative target composed of a group of concentric circles and a central positioning radar angle reflector is designed. Secondly, the corresponding relationship of radar and image characteristic matrix is obtained after employing the RANSAC method to fit radar and image detection information; then, the homographic matrix is solved to realize radar and image data matching. Thirdly, the YOLOv5 detector is used to track the ship motion in the image sequence. The visual measurement model based on continuous object tracking is established to extract the ship motion parameters. Finally, the motion intention of the ship is predicted by integrating the extracted ship motion features with the position information of the shallow layer using a Bayesian framework. Many experiments on real data sets show that our proposed method is superior to the most advanced method for ship intention identification at intersections

Multi-Sensor-Based Hierarchical Detection and Tracking Method for Inland Waterway Ship Chimneys

In the field of automatic detection of ship exhaust behavior, a deep learning-based multi-sensor hierarchical detection method for tracking inland river ship chimneys is proposed to locate the ship exhaust behavior detection area quickly and accurately. Firstly, the primary detection uses a target detector based on a convolutional neural network to extract the shipping area in the visible image, and the secondary detection applies the Ostu binarization algorithm and image morphology operation, based on the infrared image and the primary detection results to obtain the chimney target by combining the location and area features; further, the improved DeepSORT algorithm is applied to achieve the ship chimney tracking. The results show that the multi-sensor-based hierarchical detection and tracking method can achieve real-time detection and tracking of ship chimneys, and can provide technical reference for the automatic detection of ship exhaust behavior

Motion Planning for an Unmanned Surface Vehicle with Wind and Current Effects

Aiming at the problem that unmanned surface vehicle (USV) motion planning is disturbed by effects of wind and current, a USV motion planning method based on regularization-trajectory cells is proposed. First, a USV motion mathematical model is established while considering the influence of wind and current, and the motion trajectory is analyzed. Second, a regularization-trajectory cell library under the influence of wind and current is constructed, and the influence of wind and current on the weight of the search cost is analyzed. Finally, derived from the regularization-trajectory cell and the search algorithm, a motion planning method for a USV that considers wind and current effects is provided. The experimental results indicate that the motion planning is closer to the actual trajectory of a USV in complex environments and that our method is highly practicable