Traffic identifier detection method based on multi-scale circulation attention network

Abstract

The invention discloses a traffic identifier detection method based on multi-scale circulation attention network. The method comprises the following steps: firstly, building a traffic identifier detection model, wherein the traffic identifier detection model is formed by compounding a convolutional neural network model feature extraction model for carrying out image feature extraction and a multi-scale circulation attention network model for improving small-target detection accuracy; then training the traffic identifier detection model by utilizing a reasonable training sample so as to acquirea trained traffic identifier detection model; and inputting to-be-detected images into the trained traffic identifier detection model during testing so as to acquire a detection result. According tothe method disclosed by the invention, by applying an encoder / decoder structure, the acquired features are enhanced, small targets are detected by using a multi-scale attention structure, and referring to a residual difference structure, the problems of gradient disappearance and gradient explosion are solved. Compared with the other advanced traffic identifier detection methods, the method disclosed by the invention has the advantage of competitiveness.

Description

technical field [0001] The invention relates to traffic identifier detection technology, in particular to a traffic identifier detection method based on a multi-scale cyclic attention network. Background technique [0002] Traffic sign detection is a crucial and challenging topic in both academia and industry, and has been a hot area of ​​research for the past decade. Real-time and powerful traffic sign detection technology can reduce driver stress, thereby significantly improving driving safety and comfort. For example, it can alert the driver of the current speed limit, preventing him from exceeding the speed limit. In addition, it can be integrated into automated driving systems (ADS) and advanced driver assistance systems (ADAS) to reduce driving stress. [0003] There are various algorithms for traditional traffic sign detection, including adaboost, support vector machine, Hough transform, etc., which use color, texture, line and other low-level features to detect the...

AI Technical Summary

Problems solved by technology

However, although a huge traffic sign dataset, such as the Tsinghua-Tencent100K dataset, has been constructed, deep learning has not shown significant advantages in traffic sign detection, partly due to the small size of traffic signs and the lack of effective contextual information. Take advantage of

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