Visible light, infrared and radar fusion target detection method based on deep learning

Abstract

The invention discloses a visible light, infrared and radar fusion target detection method based on deep learning, and the method comprises the steps: automatically carrying out the analysis and processing of data collected by a visible light camera, a thermal infrared camera and a laser radar, carrying out the fusion of the pretreatment stages of visible light and infrared, achieving the conversion from the point cloud of the laser radar to an aerial view, and carrying out the fusion of the point cloud and the aerial view. And then automatic pyramid-type cross-stage feature extraction and bifurcation are carried out on the fused image and the aerial view, bifurcation target detection is carried out, graph matching is carried out, and finally target detection fusing three modes of visible light, infrared and radar is realized, so that the detection accuracy is improved, the robustness is relatively good, the method can adapt to scene change and dynamic change of a load, and the method is suitable for large-scale popularization and application. Along with the increase of training set target types and the increase of training sets, the method has wider detection performance.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence target detection and matching, in particular to a deep learning-based fusion target detection method of visible light, infrared and radar. Background technique [0002] Target detection is a major research issue in the field of computer vision. Image segmentation, target tracking, and target behavior analysis are all based on target detection. With the development of deep learning algorithms based on convolutional neural networks, target detection has made great breakthroughs. Target detection algorithms based on visible light camera images have been widely used. As a kind of sensor, visible light cameras have the advantages of realistic geometric shape, strong stereoscopic effect, and good resolution, but also have their limitations. For example, visible light cameras are passive cameras. When disturbed by low light or rain, snow, fog, etc., the image quality will drop significant...

AI Technical Summary

Problems solved by technology

[0003] The information in the scene obtained by single and unfused target detection is not rich enough, nor can it help to better understand the target and the surrounding environment, and cannot adapt to complex scene changes and dynamic changes. With the increase of training set target types and training As the set increases, the detection efficiency and performance may gradually decrease

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