Night infrared pedestrian detection method and system based on improved YOLOv3

A pedestrian detection and infrared technology, applied in the field of deep learning target detection, can solve the problems of low target feature extraction and feature expression capabilities of the target detection model, lack of color in infrared images, and large impact of weather changes, etc., to reduce background pixel values, The effect of reducing background feature information, improving feature extraction ability and feature expression ability

CN114332942APending Publication Date: 2022-04-12WUHAN UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2022-04-12

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Abstract

The invention discloses a night infrared pedestrian detection method and system based on improved YOLOv3, and the method comprises the steps: collecting a plurality of infrared pedestrian detection data sets through a plurality of infrared cameras, carrying out the pixel value contrast enhancement of the data sets, increasing the pedestrian pixel values, and reducing the background pixel values; a night infrared pedestrian detection network model YOLOv3-SAB is improved and constructed based on YOLOv3, a stem down-sampling module and asymmetric convolution are introduced to improve the network feature extraction capability and feature expression capability, calculation parameters in a bottleneck residual error reduction model are introduced, and the pedestrian detection speed of the model is improved; a specific prior aiming frame is generated through clustering by using a mean value clustering algorithm, and the model target positioning precision is improved; cIoU is used as a YOLOv3-SAB network bounding box regression loss function, so that model convergence is accelerated, and the accuracy of a prediction box is improved; a YOLOv3-SAB network is trained to generate a night infrared pedestrian detection model; and performing real-time infrared pedestrian detection at night by using the night infrared pedestrian detection model. According to the invention, the detection precision and the detection speed of pedestrians at night are effectively improved.
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Description

technical field

[0001] The invention belongs to the technical field of deep learning target detection, and more specifically relates to a nighttime infrared pedestrian detection method and system based on an improved deep learning target detection model (YOLOv3). Background technique

[0002] As an important branch of the field of machine vision, deep learning target detection has been widely used in the fields of automobile assisted driving, intelligent monitoring and military strike, and has become a research hotspot in the field of computer vision. The infrared image is obtained by the infrared imaging system according to the thermal radiation imaging of the object. The infrared image has the advantages of strong anti-interference ability and is not easily affected by harsh environments. Compared with the use of visible light for target detection, infrared target detection has higher research and application value. .

[0003] In recent years, with the continuous emergenc...

Examples

Embodiment Construction

[0042] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0043] please see figure 1 , a kind of night-time infrared pedestrian detection method based on improved YOLOv3 provided by the present invention, comprises the following steps:

[0044] Step 1: Build a deep learning target detection network YOLOv3-SAB;

[0045] please see figure 2 , the present embodiment constructs the deep learning target detection network YOLOv3-SAB based on the YOLOv3 neural network algorithm in deep learning, improves the pedestrian feature extraction algorithm part in the YOLOv3 neural network algorithm, and obtains a new deep learn...