Determination of low image quality of a vehicle camera caused by heavy rain
A method for detecting heavy rain in images includes providing a series of images as input information, the images relating to a scene external to a vehicle viewed through a pane of the vehicle. A measure of blur is evaluated in an area of interest in the images. The method also includes analyzing the course of the measured blur values over time in order to detect transitions between images with low blur values and images with high blur values or vice versa. Heavy rain may then be detected from the detected transitions and information output in response to the heavy rain being detected.
1. A method for detecting heavy rain in images, comprising:
providing a series of images as input information, the images relating to a scene external to a vehicle viewed through a pane of the vehicle;
evaluating a measure of blur in an area of interest in the images, wherein the area of interest includes a part of a surface of a road in front of the vehicle and wherein the measure of blur is calculated from edges found in the area of interest utilizing a HAAR wavelet transform, wherein a percentage of Roof- and Gstep- are used as a measure of blur;
detecting transitions between images with low blur values and images with high blur values or vice versa by analyzing the course of the measured blur values over time;
detecting heavy rain from the detected transitions, wherein a state of heavy rain is determined if the number of transitions detected with a determined time period exceeds a threshold ratio; and
providing an output information in the case where heavy rain is detected.
2. A method as set forth in claim 1 , wherein detecting transitions between images with low blur values and images with high blur values or vice versa comprises utilizing a threshold value to detect a transition between images having a local minimum blur value and subsequent images having a local maximum blur value or vice versa.
3. A method as set forth in claim 1 wherein edges are classified as Dirac, Roof, Astep, or Gstep types and the measure of blur takes into account Roof, Astep and Gstep type edges.
4. A device for detecting heavy rain in images, comprising:
an input unit;
a processing unit; and
an output unit;
wherein, the input unit is configured to provide the processing unit with a series of images, the images relating to a scene external to a vehicle viewed through a pane of the vehicle;
wherein, the processing unit is configured to
evaluate a measure of blur in an area of interest in the images, wherein the area of interest includes a part of a surface of a road in front of the vehicle and wherein the measure of blur is calculated from edges found in the area of interest utilizing a HAAR wavelet transform, wherein a percentage of Roof- and Gstep- are used as a measure of blur,
detect transitions between images with low blur values and images with high blur values or vice versa by analyzing the course of the measured blur values over time, and
detect heavy rain from the detected transitions, wherein a state of heavy rain is determined if the number of transitions detected with a determined time period exceeds a threshold ratio; and
wherein, the output unit is configured to provide information in response to heavy rain being detected.
5. A device as set forth in claim 4 further comprising a camera configured to record the series of images and transmit the recorded images to the input unit.
6. A device as set forth in claim 5 wherein the at least one camera is not focused on the pane of the vehicle.