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A pedestrian detection method in unmanned driving based on improved YOLOv2

An unmanned driving and pedestrian detection technology, applied in the field of pedestrian detection, can solve the problems of low detection rate, complex calibration of training samples, and handling pedestrians with multiple postures, so as to improve the accuracy and ensure the detection speed

Active Publication Date: 2018-12-11
WUHAN UNIV OF TECH
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Problems solved by technology

This type of method allows the elastic connection of the geometric relationship of the component units through the decomposition of the target features, so that the missed detection rate is greatly reduced, but this type of method has complex calibration of training samples and the problem of dealing with pedestrians with multiple poses.

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  • A pedestrian detection method in unmanned driving based on improved YOLOv2
  • A pedestrian detection method in unmanned driving based on improved YOLOv2
  • A pedestrian detection method in unmanned driving based on improved YOLOv2

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[0033] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0034] The basic process of the present invention is as figure 1 As shown, first, the YOLOv2 network is trained through the KITTI dataset to obtain the training model. Then, capture the video through the car camera, use each frame in the video as the input of the YOLOv2 network, and import the training model into the YOLOv2 network. Then run the network to initially obtain the position information and probability of the detected pedestrians. Finally, after filtering some proposal frames that are unlikely to contain pedestrian target areas, the final pedestrian detection frame is obtained by using...

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Abstract

The invention discloses a pedestrian detection method in unmanned driving based on an improved YOLOv2. Firstly, a YOLOv2 network is trained by a KITTI dataset, and a training model is obtained. Then,the video is captured by a car camera, each frame in the video is used as the input of the YOLOv2 network, and the training model is imported into the YOLOv2 network. Then the network is run and the location information and probability of pedestrians are obtained. At last, after suggestion boxes which can not contain the pedestrian target area are screened out, the final pedestrian detection boxesare obtained by using the non-maximum suppression. The method can be effectively applied to pedestrian detection in unmanned driving.

Description

technical field [0001] The invention relates to the technical field of pedestrian detection, in particular to a pedestrian detection method in unmanned driving based on improved YOLOv2. Background technique [0002] The pedestrian detection algorithm in unmanned driving not only needs to judge whether there are pedestrians in the image captured by the camera, but also accurately locates the pedestrian target. From the perspective of feature learning, pedestrian detection can be divided into detection algorithms based on shallow machine learning and detection algorithms based on deep learning [5]. The pedestrian detection technology based on shallow machine learning mainly achieves the purpose of identifying and locating pedestrians by analyzing the dynamic and static characteristics of pedestrians, manually designing features to describe the characteristics, and combining corresponding image processing and pattern recognition algorithms. The pedestrian detection technology ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/10G06F18/23213G06F18/24147
Inventor 石英罗佳齐李振威
Owner WUHAN UNIV OF TECH
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