The present invention relates to a vessel traffic service system and a method for extracting accident data, and more particularly, to a technique that search and extracts possible event traffic data automatically from data of a concerned area without knowing exact time when a marine accident occurs. The vessel traffic service system includes: an input unit receiving conditional information for extracting accident occurrence data from a user; a storage unit storing at least one of an automatic identification system (AIS) signal, a radar signal, and a camera image signal; and a control unit extracting an event occurrence part from at least one of the AIS signal, the radar signal, and the camera image signal of a point and a time at which an accident occurs in accordance with the conditional information to generate an accident candidate list.

A radar apparatus is provided, which can enhance resolution by data processing. The radar apparatus includes a radar wave transmitter, a radar wave receiver, and an analyzing module. The analyzing module analyzes data received by the radar wave receiver, stores amplitude data and speed data of a target object for every cell in a predetermined coordinate system, and processes, based on the data, the amplitude data of an observing cell in the predetermined coordinate system associated with speed. The analyzing module determines whether an absolute value of a speed difference between adjacent cells and adjacent to the observing cell while the observing cell is located therebetween, is a threshold or higher, and the analyzing module performs processing of reducing the amplitude of the observing cell if the absolute value is the threshold or higher.

A method and system. The method includes receiving weather radar data. The method further includes filtering out weather from the weather radar data to provide filtered radar data. Additionally, the method includes determining whether the filtered radar data includes any non-weather targets. If any of the non-weather targets is a hazard target, the method includes storing data associated with the hazard target in a hazard data structure.

A method and system. The method includes receiving weather radar data. The method further includes filtering out weather from the weather radar data to provide filtered radar data. Additionally, the method includes determining whether the filtered radar data includes any non-weather targets. If any of the non-weather targets is a hazard target, the method includes storing data associated with the hazard target in a hazard data structure.

A ground penetrating radar and deep learning-based underground pipeline detection method and system. Said method comprises: acquiring sample data of known underground pipelines by means of a ground penetrating radar, and establishing an GPR B-scan dataset according to the sample data; performing training according to the GPR B-scan dataset to obtain a YOLOv3 model, the YOLOv3 model being used for identifying hyperbolic data of the underground pipelines; detecting underground pipeline targets in a real radar image by means of the YOLOv3 model; and precisely locating the positions of pipelines by means of an RTK measurement instrument. Said method is based on a ground penetrating radar and a YOLOv3 model, and can accurately identify hyperbolic targets of pipelines in ground penetrating radar images, thereby improving the detection efficiency and reducing time costs. The present invention can be widely applied to the field of engineering non-destructive testing.

Techniques are discussed herein for analyzing radar data to determine that radar noise from one or more target detections potentially conceals additional objects near the target detection. Determining whether an object may be concealed can be based at least in part on a radar noise level based on a target detection, as well as distributions of radar cross sections and/or doppler data associated with particular object types. For a location near a target detection, a radar system may determine estimated noise levels, and compare the estimated noise levels to radar cross section probabilities associated with object types to determine the likelihood that an object of the object type could be concealed at the location. Based on the analysis, the system may determine a vehicle trajectory or otherwise may control a vehicle based on the likelihood that an object may be concealed at the location.

Techniques are discussed herein for analyzing radar data to determine that radar noise from one or more target detections potentially conceals additional objects near the target detection. Determining whether an object may be concealed can be based at least in part on a radar noise level based on a target detection, as well as distributions of radar cross sections and/or doppler data associated with particular object types. For a location near a target detection, a radar system may determine estimated noise levels, and compare the estimated noise levels to radar cross section probabilities associated with object types to determine the likelihood that an object of the object type could be concealed at the location. Based on the analysis, the system may determine a vehicle trajectory or otherwise may control a vehicle based on the likelihood that an object may be concealed at the location.