Vehicle blind area detection method and device

Abstract

The embodiment of the invention provides a vehicle blind area detection method and a vehicle blind area detection device. The method comprises the following steps: acquiring preset thermal image dataof a blind area in real time and performing recognition processing on the thermal image data to acquire the obstacle characteristic of each frame of image; and determining whether to send out an alarmsignal according to the obstacle characteristic. According to the vehicle blind area detection method and the vehicle blind area detection device, the preset thermal image data of the blind area is acquired in real time, the thermal image data is subjected to recognition processing, and the obstacle characteristic of each frame of image is acquired; and whether to send out the alarm signal is determined according to the obstacle characteristic, so that detection recognition on pedestrians in the blind area is realized and the safety accident is avoided.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of monitoring, and in particular, to a method and device for detecting a blind spot of a vehicle. Background technique [0002] Cars have blind spots, especially large vehicles. Many traffic accidents are often caused by pedestrians breaking into the blind spots of vehicles, and the drivers do not realize it. Helping the driver monitor the blind spot and detecting whether there are pedestrians or vehicles intruding in the blind spot can effectively reduce the occurrence of such accidents. [0003] At present, most vehicles are not equipped with equipment with blind spot detection function, and a small number of vehicles are equipped with equipment with blind spot detection function using ordinary cameras. [0004] Ordinary cameras are susceptible to lighting, occlusion, complex and changeable environments, etc., resulting in a certain probability of missed and misjudged blind spot...

AI Technical Summary

Problems solved by technology

[0004] Ordinary cameras are susceptible to lighting, occlusion, complex and changeable environments, etc., resulting in a certain probability of missed judgments and misjudgments in the blind spot detection function.

[0005] For example, the diversity of pedestrians, some wearing hats, some holding children, some riding tricycles, some holding umbrellas, etc. It is difficult for image recognition algorithms based on ordinary cameras to cover all situations.

Another example is daytime, night, snowy, rainy, backlighting and other factors, which lead to ordinary cameras not being able to obtain a complete or clearer image in some cases, and the image recognition algorithm loses its effect

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