Vehicle target detection method and system based on YOLOv2

A target detection and vehicle technology, applied in the field of vehicle recognition, can solve the problems of slow speed and low vehicle detection accuracy, and achieve the effects of fast detection rate, enhanced clarity, and improved contrast.

Inactive Publication Date: 2018-12-07
TAIHUA WISDOM IND GRP CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of this, the present invention provides a vehicle target detection method and system base

Method used

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  • Vehicle target detection method and system based on YOLOv2
  • Vehicle target detection method and system based on YOLOv2
  • Vehicle target detection method and system based on YOLOv2

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0083] This embodiment provides a vehicle target detection method based on YOLOv2, which is used to identify all vehicle targets in the image according to real-time traffic video data or snapshot image data. This method mainly includes two parts: model training and actual detection. Before the actual video or picture is detected, a large number of sample images need to be trained to obtain the vehicle detection model. After the training is completed, the video data can be input into the trained vehicle. In the vehicle detection model, the result map can be output directly.

[0084] Specifically, such as figure 1 Shown is a flow chart of the YOLOv2-based vehicle target detection method, which includes the following steps:

[0085] S101: Obtain sample traffic video data and sample snapshot image data;

[0086] Among them, the sample traffic video data and sample snapshot image data can be obtained through various channels, such as video monitoring data and snapshot image data ...

Embodiment 2

[0116] On the basis of Embodiment 1, this embodiment provides a preferred vehicle target detection method based on YOLOv2. For related matters, reference may be made to the description in Embodiment 1. Specifically, such as figure 2Shown is another YOLOv2-based vehicle target detection method flow chart. The method includes:

[0117] S201: Obtain sample traffic video data and sample snapshot image data;

[0118] Among them, the sample traffic video data and sample snapshot image data can be obtained through various channels, such as video monitoring data and snapshot image data provided by the Transportation Bureau. This method does not limit the channels for obtaining sample traffic video data and sample snapshot image data , as long as there is a vehicle in the video data and the captured picture data.

[0119] S202: Divide the sample traffic video data into frame images, and use the sample snapshot image data together as sample images; that is, convert the data in vide...

Embodiment 3

[0161] On the basis of Embodiment 1 and Embodiment 2, this embodiment provides a vehicle target detection system based on YOLOv2.

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Abstract

The invention discloses a vehicle target detection method and system based on YOLOv2, which comprises the steps of obtaining sample traffic video data; dividing the sample traffic video data into frame images to serve as sample images; performing noise reduction, shadow elimination, local histogram equalization and local invariant analysis on the sample images to obtain training images; inputtingthe training images into a preset YOLOv2 neural network model for training to obtain a vehicle detection model; obtaining real-time vehicle video data; performing noise reduction, shadow elimination,ghost elimination, local smoothing and local invariant analysis on the real-time vehicle video data to obtain secondarily processed real-time vehicle video data; segmenting the secondarily processed real-time vehicle video data into frame images, and inputting the frame images into the vehicle detection model to obtain a result map. The vehicle detection accuracy and the detection speed can be improved according to the vehicle target detection method and system based on YOLOv2 provided by the invention.

Description

technical field [0001] The present invention relates to the field of vehicle recognition, and more specifically, to a method and system for detecting vehicle targets based on YOLOv2. Background technique [0002] The development of science and technology provides richer technical support for the construction of cities. The recognition of specific targets in images has always been the focus technology in the field of computer vision. Among them, vehicle target detection is of great significance in both civilian and military applications. In terms of civilian use, vehicle recognition has promoted the application of intelligent transportation, smart parking, security and other fields; in military terms, vehicle recognition can identify and track vehicles (chariots, armored vehicles, etc.) Targets, enemy dynamic monitoring and other aspects play a key role. [0003] With the advancement of urbanization, the increase of car ownership, the improvement of urban traffic and vehicle...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06K9/40G06K9/44G06K9/36G06K9/34
CPCG06V20/49G06V20/54G06V10/273G06V10/20G06V10/30G06V10/34G06V2201/08G06F18/214
Inventor 李鹏马述杰
Owner TAIHUA WISDOM IND GRP CO LTD
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