Modified local sensitive hash vehicle retrieval method based on multitask deep learning

Abstract

The invention discloses a modified local sensitive hash vehicle retrieval method based on multitask deep learning. A multitask end-to-end convolution neural network is used to identify a vehicle model, a vehicle system, a vehicle logo, a color and a license plate simultaneously in a subsection parallel mode. A network module for extracting vehicle image example features based on a characteristic pyramid and an algorithm by using a modified local sensitive hash sorting algorithm to sort the vehicle characteristics in a database, and a cross-modal text retrieval method when a retrieval vehicle image can not be acquired are included. The multitask end-to-end convolution neural network and the modified local sensitive hash vehicle retrieval method are provided, the automation level and the intelligence level of vehicle retrieval can be improved effectively, little storage space is used, and image retrieval requirements in a big data era are met by using a quicker retrieval speed.

Description

technical field [0001] The invention relates to the application of computer vision, pattern recognition, information retrieval, multi-task learning, similarity measurement, deep self-encoding convolutional neural network and deep learning technology in the field of image retrieval, especially to a modified local Sensitive Hash Vehicle Retrieval Method. Background technique [0002] With the rapid development of society and economy, motor vehicles have become an essential tool for criminals and terrorists to engage in illegal activities while increasingly becoming an indispensable means of transportation for people's daily life. All provinces and inter-city expressways and main roads, city entrances and exits, and major traffic arteries have deployed bayonet equipment to collect information from passing vehicles. However, current bayonets are generally based on license plate recognition technology. Once a suspected vehicle uses fake License plates, sets of cards, no license ...

AI Technical Summary

Problems solved by technology

[0010] Aiming at the low level of automation and intelligence in the existing vehicle retrieval technology, lack of deep learning, difficulty in obtaining accurate retrieval results, large storage space consumption of retrieval technology, slow retrieval speed and difficulty in meeting the image retrieval needs in the era of big data, this paper The invention proposes an end-to-end vehicle image retrieval method through layered depth search based on deep self-encoded convolutional neural network, which improves the automation and intelligence level of the retrieval system by using deep learning methods and simultaneously enables image recognition, feature acquisition, The perfect combination of retrieval efficiency enables the entire retrieval system to obtain accurate retrieval results, and the use of sparse coding reduces the system's dependence on memory and speeds up retrieval, thereby meeting the image retrieval requirements in the era of big data

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