Vehicle detection method based on improved YOLOv3

A vehicle detection and vehicle technology, which is applied in the field of computer vision, can solve the problems of insufficient real-time performance and precision, and detection time reduction, and achieve the effect of enriching the number, improving efficiency, and ensuring accuracy
CN111428550AInactive Publication Date: 2020-07-17CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
Publication Date
2020-07-17
Estimated Expiration
Not applicable Β· inactive patent

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Abstract

The invention discloses a vehicle detection method based on improved YOLOv3. According to the method, a feature weight optimization structure SEnet is introduced into a residual structure of a featureextraction network Darknet53 in YOLOv3 to optimize features extracted by the YOLOv3; residual error module reduction is performed on the feature extraction network after the SEnet structure is introduced; and the heights and the widths of vehicle labels in a vehicle data set are clustered by adopting a k-means method so as to optimize the original YOLOv3 target detection priori box. During batchtesting, the improved YOLOv3 has a vehicle detection map of 96.49% and a detection speed of 45 graphs / second, so that the requirements of real-time performance and accuracy of traffic detection are met, and a high application value is achieved in traffic detection.
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Description

technical field

[0001] The invention relates to the field of computer vision and the technical field of intelligent transportation information technology, specifically, a vehicle detection and recognition method based on the improved YOLOv3 model. Background technique

[0002] The traditional vehicle detection method that is currently widely used requires manual participation in feature selection and other work, which has poor generalization ability and low recognition accuracy.

[0003] The YOLOv3 deep learning network consists of two networks, Darknet53 and YOLO. It learns target features through convolution and performs multi-scale fusion of features. It can automatically learn features and enhance the expressive ability of features. It is better than many machine learning methods in the field of target detection. Although it achieves higher recognition speed and recognition accuracy, it still cannot meet the real-time detection and recognition requirements of vehicles un...

Claims

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