Intelligent detection method for multiple types of diseases of near-water bridge and unmanned ship equipment

Abstract

The invention discloses an intelligent detection method for multiple types of diseases of a near-water bridge and unmanned ship equipment. According to the method, an infrastructure disease target detection network CenWholeNet and a parallel attention module PAM based on the bionic thought are included, and the CenWholeNet is an Anchor-free target detection network based on deep learning, mainly comprises a backbone network and a detector and is used for automatically detecting diseases in collected images with high precision. The PAM introduces an attention mechanism into the neural network, comprises two parts of space attention and channel attention, and is used for enhancing the expression ability of the neural network. The unmanned ship equipment comprises a ship body module, a video acquisition module, a laser radar navigation module and a ground station module, supports laser radar navigation without GPS information, remote real-time transmission of video information and high-robustness real-time control, and is used for automatically acquiring bridge bottom information. The method can be widely applied to disease detection of areas with weak GPS signals and complex environment, such as the bottoms of small and medium-sized bridges.

Description

technical field [0001] The invention belongs to the field of structural health detection in civil engineering, and in particular relates to an intelligent detection method for multiple types of diseases of near-water bridges and unmanned ship equipment. Background technique [0002] During the service process of engineering structures, many diseases will occur due to the influence of load and environment. Once these diseases are generated, they will easily accumulate and expand, thereby affecting the service life and overall safety of the structure, and even affecting the safety of people's lives and property. In recent years, cases of structural damage such as bridge collapse due to lack of effective inspection and maintenance are common. Therefore, regular inspection and maintenance of the structure is very important. [0003] Traditional infrastructure disease detection methods are mainly manual. These methods require the use of complicated tools, and there are problems...

AI Technical Summary

Problems solved by technology

However, the GPS signal in the bottom area of ​​small and medium bridges with very low headroom is often very weak, and the internal situation is also very complicated. There will be risks such as signal loss and collision damage when drones fly in.

And some areas are very narrow, there may be poisonous gas, and it is difficult to reach artificially

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