Aerially-photographed vehicle real-time detection method based on deep learning

Abstract

The invention provides an aerially-photographed vehicle real-time detection method based on deep learning and mainly aims to solve the problem that in the prior art, it is difficult to perform precisedetection on an aerially-photographed vehicle target under a complicated scene on the basis of guaranteeing instantaneity. The method comprises the implementation steps that 1, an aerially-photographed vehicle dataset is constructed; 2, a multi-scale feature fusion module is designed, and a RefineDet real-time target detection network based on deep learning is optimized in combination with the module, so that an aerially-photographed vehicle real-time detection network is obtained; 3, a cross entropy loss function and a focus loss function are utilized to train the aerially-photographed vehicle real-time detection network in sequence; and 4, a trained detection model is used to detect a vehicle in a to-be-detected aerially-photographed vehicle video. According to the method, the designedmulti-scale feature fusion module can effectively increase the information utilization rate of the aerially-photographed vehicle target, meanwhile, the aerially-photographed vehicle dataset can be trained more sufficiently by use of the two loss functions, and therefore the detection accuracy of the aerially-photographed vehicle target under the complicated scene is improved.

Description

technical field [0001] The invention belongs to the technical field of image recognition, and relates to a real-time vehicle detection method, in particular to a deep learning-based real-time aerial vehicle detection method, which can be used for real-time detection of road vehicles in low-altitude aerial photography scenes. Background technique [0002] Vehicle real-time detection refers to the process of real-time detection of vehicle targets in video or images to obtain the location and category information of vehicle targets. Aerial vehicle real-time detection refers to the real-time detection of road vehicles in the UAV aerial photography scene. As an important part of the intelligent transportation system, it plays an important role in the acquisition of real-time road conditions, highway inspections, and illegal parking processing. At present, commonly used aerial vehicle detection methods can be divided into the following categories: aerial vehicle detection based on...

AI Technical Summary

Problems solved by technology

However, the feature scale used by this method for feature fusion is relatively large, and the information utilization rate for smaller aerial vehicle targets is not high; and this method uses the cross-entropy loss function during training, which has a large impact on the aerial vehicle data set. All training samples adopt a uniform treatment strategy, resulting in insufficient training of difficult vehicle samples, so it is impossible to accurately detect aerial vehicle targets in complex scenes (including difficult vehicle samples)

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