Night infrared pedestrian detection method and system based on improved YOLOv3

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.

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...

AI Technical Summary

Problems solved by technology

[0004] The target detection accuracy of the two-stage detection model is high, but the detection speed is slow, and the real-time performance is difficult to meet; the target detection of the single-stage detection model has good real-time performance and fast detection speed, but the detection accuracy is low

Pedestrian detection using visible light is greatly affected by light conditions and weather changes, and has great limitations in weak light conditions at night and harsh weather conditions. Pedestrian detection using infrared technology is less affected by light and the environment, but the infrared image is due to the presence of color. The defects of missing and insufficient texture make the target detection model have low target feature extraction ability and feature expression ability

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