An automatic ship detection method for inland river bridge areas

Abstract

The invention discloses an automatic detection method for ships in an inland bridge area, which includes: 1) collecting real traffic monitoring video in the water area of ​​the inland bridge area to obtain original sampling picture data; 2) manually marking the original sampling picture data to form Mark the image; 3) use the deep convolutional neural network to learn the features of the marked image, classify the learned features, and obtain a trained deep learning model; 4) use the trained deep convolutional neural network model to The ship is detected, the position of the ship is framed by a rectangular frame, and the category of the ship is indicated. This method has good adaptability to different weather, different light conditions and dark conditions at night, can achieve a relatively high accuracy rate, and the processing speed meets the real-time requirements.

Description

Technical field [0001] The present invention relates to the technical field of computer vision and the technical field of traffic monitoring systems, in particular to an automatic detection method for ships used in inland river bridge areas. Background technique [0002] As an important means of transportation, waterway transportation occupies an important position in the national transportation system. With the rapid development of the country's economy, the trade volume is increasing rapidly, and the busyness of waterway transportation is increasing day by day. At the same time, more and more inland river bridges have also proved that the country's economy has taken off. However, while the increasing number of inland river bridges facilitates land transportation, they have also become an important factor threatening the safety of waterway transportation. Once the ship collides with the bridge, the lighter will cause damage to the ship and the bridge, and the severer will cause...

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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