Method and device for detecting dirt of binocular ADAS camera

Abstract

The invention discloses a method and device for detecting dirt of a binocular ADAS camera. The dirt can be efficiently detected. The method comprises the steps that A, whether a vehicle is in a movingstate or not is judged, if yes, the step B is executed, and if not, processing is not performed; B, judging whether the number of motion frames is greater than a threshold value 1 or not, if so, considering that the current vehicle is in motion, carrying out the step C, and if not, do not carrying out processing; C, whether the images of the two cameras are relatively consistent is judged, if yes, no dirt exists, and if not, the step D is executed, specifically, the step C comprises the steps that C1, the structural similarity is judged, if yes, the step D is executed, and otherwise, the stepC2 is executed; C2, gray scale comparison is carried out, if it is judged that dirt exists through gray scale comparison, the step D is carried out, and otherwise it is judged that no dirt exists; and D, judging whether the frame number of the dirty image is greater than a threshold value 2, if so, judging that dirt exists, and if not, judging that no dirt exists.

Description

technical field [0001] The invention relates to an advanced driving assistance system, in particular to a binocular camera. Background technique [0002] At present, binocular cameras have been used in automobile assisted driving for many years, including Subaru and other models, which have been mass-produced on a large scale. As a binocular ADAS (Advanced Driving Assistance System) camera, because it can recognize both And recording function, and it can have accurate depth ranging function, which has a broader market prospect than the widely used monocular camera. [0003] However, as a vehicle-mounted device, dirt often blocks the front of the camera lens and affects the image quality, resulting in errors in parallax extraction, stereo matching, obstacle detection, pedestrian and vehicle recognition, etc., which have a great impact on the function of the device. In recent years, there have been many technical solutions in the industry trying to solve this problem. For exa...

AI Technical Summary

Problems solved by technology

Some people also proposed to use the method of depth camera dirt detection, and use the depth information provided by the depth camera to improve the false detection caused by the lack of distance information. However, this method has high requirements for depth image evaluation and is not simple and practical enough.

Images