Automatic ship detection method for inland river bridge areas

Abstract

The present invention discloses an automatic ship detection method for inland river bridge areas, the method comprises: 1) collecting traffic surveillance videos of real inland river bridge areas andobtaining original sampled image data; 2) performing manual label to the original sampled image data and forming a label image; 3) using depth convolution neural network to learn characteristics of the label image, classifying the characteristics learned, and obtaining a trained deep learning model; 4) using the trained deep convolution neural network model to detect ships in real scene, framing out position of the ships with rectangular frames, and indicating classification of the ships. The method has good adaptability to different weather, different lighting conditions and nighttime dark conditions, and can achieve relatively high accuracy, and has processing speed reaching real-time requirements.

Description

technical field [0001] The invention relates to the technical field of computer vision and the technical field of traffic monitoring system, in particular to an automatic ship detection method used in inland river bridge areas. Background technique [0002] As an important mode of transportation, waterway transportation occupies an important position in the national transportation system. With the rapid development of the national economy and the rapid growth of trade volume, the busyness of waterway transportation is increasing day by day. At the same time, the growing number of inland river bridges also testifies to the country's economic take-off. However, the increasing number of inland river bridges has also become an important factor threatening the safety of waterway transportation while facilitating land transportation. Once the ship collides with the bridge, the light one will cause damage to the ship and bridge, and the severe case will lead to bridge collapse, sh...

AI Technical Summary

Problems solved by technology

However, the data information obtained in this way is not intuitive enough

Although bridges are currently equipped with surveillance cameras, they still rely on manual monitoring of surveillance video. When the traffic flow is heavy, if people make misjudgments or missed judgments due to factors such as fatigue, laxity, or lack of experience, the probability of accidents will increase.

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