Deep learning-based vehicle detection method

A technology of vehicle detection and deep learning, applied in the field of vehicle detection based on deep learning, can solve problems such as low efficiency and poor generalization ability

Active Publication Date: 2018-11-16
XIAN UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a vehicle detection method based on deep learning, which solves the problem that the existing feature-based vehicle detection

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  • Deep learning-based vehicle detection method
  • Deep learning-based vehicle detection method
  • Deep learning-based vehicle detection method

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Embodiment Construction

[0061] The present invention will be described in detail below with reference to the drawings and specific embodiments.

[0062] A vehicle detection method based on deep learning, such as figure 1 As shown, follow the steps below:

[0063] Step 1. Construct an image library with annotations and labels as the training sample set and the test sample set; specifically:

[0064] Use the open source calibration software LabelImg to construct an image library with labels and labels as the training sample set and test sample set, where the sample set contains vehicle photos (including background) taken under the same weather conditions in different scenarios and in different weather conditions. It is to record the coordinates of the upper left corner and the lower right corner of the vehicle in an entire image. The label refers to the category of the vehicle at each given vehicle position;

[0065] Step 2. Construct an improved Faster R-CNN model. The improved Faster R-CNN model consists of...

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Abstract

The invention discloses a deep learning-based vehicle detection method which is used for conducting vehicle detection in a complex environment by combining Edge Boxes and an improved Faster R-CNN model. The method comprises the steps that an image is processed by using the Edge Boxes, and an accurate vehicle candidate area is extracted primarily; and then the candidate area is input to the improved Faster R-CNN model to achieve precise positioning on vehicles, and a final detection result is obtained through classification and discrimination. In order to enhance the detection capacity on the small-size vehicles by the model and the discrimination capacity of the model, different layers of convolutional features are combined, some detail information of the vehicles is supplemented, a difficult negative sample mining strategy is added in the training stage, therefore, the model can pay more attention on difficult samples, and the vehicles can be well distinguished with the backgrounds ofsuspected vehicles.

Description

Technical field [0001] The invention belongs to the technical field of computer vision recognition, and specifically relates to a vehicle detection method based on deep learning. Background technique [0002] Vehicle detection is an indispensable link in the intelligent transportation system. Effective road traffic information is collected through vehicle detection methods to obtain basic data such as traffic flow, vehicle speed, road occupancy, vehicle spacing, vehicle type, etc., to achieve monitoring purposefully , Control, analysis, decision-making, dispatching and diversion to maximize traffic resources, thereby improving the robustness and robustness of the entire intelligent transportation system. Current mainstream vehicle detection methods use HOG and SIFT methods to extract features of vehicles, and input the extracted features to support vector machines (SVM), iterators (AdaBoost) and other classifiers for vehicle detection. These methods essentially rely on artificia...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V20/584G06N3/045G06F18/214
Inventor 王林张鹤鹤
Owner XIAN UNIV OF TECH
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