Statistical filtering infrared small target detection tracking method based on YOLOv5 and Deepsort

Abstract

The invention relates to a statistical filtering infrared small target detection tracking method based on YOLOv5 and Deepsort, and the method comprises the steps: S1, collecting an infrared small target image under a complex background through an infrared imaging device, and obtaining an infrared small target image data set; s2, preprocessing the infrared small target image data set to obtain a preprocessed image data set, and dividing the preprocessed image data set into a training set and a verification set; s3, a YOLOv5s model in the YOLOv5 algorithm is trained, and a training set of a Deepsort model is obtained; step S4, inputting the training set of the Deepsort model into the Deepsort model for training, and constructing an infrared small target detection and tracking recognizer; and S5, performing real-time detection and tracking on the infrared small target by using the infrared small target detection and tracking identifier. According to the method, the infrared small target under the complex background can be accurately and quickly detected, and the robustness and the detection rate are improved. In addition, the real-time tracking effect can be achieved.

Description

technical field [0001] The invention relates to the field of target detection and tracking, and more particularly to a statistical filtering infrared small target detection and tracking method based on YOLOv5 and Deepsort. Background technique [0002] At present, target detection technology is widely used in various fields. Infrared small target detection has always been a hot spot in the field of infrared image processing, and its technical research is of great significance to military early warning, pattern recognition, image processing and other fields. However, in the actual scene, the target in the infrared image has a long imaging distance and usually appears in the form of points. In addition, the infrared signal is seriously weakened by the air during the propagation process, so that it usually exists in the form of a Gaussian distributed point target, and it does not have significant shape and texture information. In addition, small infrared targets in complex ba...

AI Technical Summary

Problems solved by technology

[0003] Traditional target detection algorithms include area frame selection, feature extraction, and classification. There are two problems: one is that the area selection strategy is not targeted and the time complexity is high; the other is poor robustness.

However, the existing detection and tracking methods that combine YOLOv5 and Deepsort tracking algorithms target large targets, such as pedestrians.

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