Abstract
Vessel recognition plays important role in ensuring navigation safety. However, existing methods are mainly based on a single sensor, such as automatic identification system (AIS), marine radar, closed-circuit television (CCTV), etc. To this end, this paper proposes a coarse-to-fine recognition method by fusing CCTV and marine radar, called multi-scale matching vessel recognition (MSM-VR). This method first proposes a novel calibration method that does not use any additional calibration target. The calibration is transformed to solve an N point registration model. Furthermore, marine radar image is used for coarse detection. A region of interest (ROI) area is computed for coarse detection results. Lastly, we design a novel convolutional neural network (CNN) called VesNet and transform the recognition into feature extraction. The VesNet is used to extract the vessel features. As a result, the MVM-VR method has been validated by using actual datasets collected along different waterways such as Nanjing waterway and Wuhan waterway, China, covering different times and weather conditions. Experimental results show that the MSM-VR method can adapt to different times, different weather conditions, and different waterways with good detection stability. The recognition accuracy is no less than 96%. Compared to other methods, the proposed method has high accuracy and great robustness.
Highlights
In recent years, the economic effects of inland waterway navigation have been highlighted than ever along with the rapid development of the Chinese economy [1], whose safety is coming to the fore with increasingly more inland vessels
Waterway safety is focusing on timely monitoring and accurately recognizing vessels that may be monitored employing many systems such as automatic identification system (AIS), marine radar, and closed-circuit television (CCTV)
Our experimental site was the Yangtze River which is the longest river in China our multi-scale matching vessel recognition (MSM-VR) method
Summary
The economic effects of inland waterway navigation have been highlighted than ever along with the rapid development of the Chinese economy [1], whose safety is coming to the fore with increasingly more inland vessels. Waterway safety is focusing on timely monitoring and accurately recognizing vessels that may be monitored employing many systems such as automatic identification system (AIS), marine radar, and closed-circuit television (CCTV). Athreshold was set for comparison with the pixel in the difference image during such threshold wasIfset set for comparison with the pixelininthe difference imageduring during binarization process. The pixel is less than the threshold, itthe can be determined as the AAthreshold was for comparison with the pixel difference image suchbinarization binarization process. Ifthe thepixel pixel lessthan than the threshold, canbe bedetermined determinedasas background; otherwise, theIfpixel can beisis regarded asthe a vessel candidate. Less threshold, ititcan thebackground; background;otherwise, otherwise,the thepixel pixelcan canbe beregarded regardedasasaavessel vesselcandidate.
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