A zero-sample classification method and device for orbital obstacles on a space-based surveillance platform

Abstract

The invention relates to a zero-sample classification method and device for orbital obstacles of a space-based surveillance platform, and relates to the technical fields of aerial surveillance and orbital obstacle detection. The device of the invention comprises a picture collector, an obstacle target extractor, a target mapping network, a semantic vector generator, a semantic vector mapping network, a cosine metric-based nearest neighbor classifier and an alarm. The method of the present invention collects railroad track pictures by drones, extracts obstacle target areas, and maps them to vectors of fixed dimensions as visual features; uses Word2vec technology to generate semantic vectors for the category names of obstacles, and then maps them to vectors of the same dimension as the visual features Semantic feature vector; establish a cosine-based nearest neighbor classifier to classify obstacles; in the training phase, use visible category obstacle samples to train the mapping network. The invention realizes the detection of unknown obstacles on the rails, improves the classification accuracy of rail obstacles, and reduces the rate of false detection and false alarms.

Description

technical field [0001] The invention belongs to the technical field of aviation surveillance, and in particular relates to a zero-sample classification method and device for orbital obstacles of a space-based surveillance platform. Background technique [0002] The space-based surveillance platform is used to ensure the normal operation of the rail transit system on a large scale throughout the day. One of the important tasks of the platform to maintain the rail transit system is to accurately detect rail obstacles. [0003] Traditional track obstacle detection is divided into real-time detection and non-real-time detection. Non-real-time detection mainly includes detection vehicle troubleshooting and manual inspection. These two methods are to carry out troubleshooting before the equipment is in operation to ensure that the track can be used normally. However, if the track failure cannot be found in time between the two investigations, it is easy to cause serious vehicle...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem that the current space-based real-time detection system is difficult to detect obstacles due to the lack or lack of training samples when detecting orbital obstacles, the present invention proposes a zero-sample classification method and device for orbital obstacles on a space-based monitoring platform. It can realize the orbital obstacle inspection of the space-based surveillance platform in the extreme situation where the obstacle target category is invisible (no training samples), and improves the classification accuracy of orbital obstacles, and reduces the false detection and false alarm rate

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