In-vehicle image recognizer

• US 9,288,381 B2
• Filed: 07/19/2013
• Issued: 03/15/2016
• Est. Priority Date: 07/27/2012
• Status: Active Grant

• Current Assignee: Nissan Motor Co., Ltd.
• Original Assignee: Clarion Co., Ltd. (Faurecia SA), Nissan Motor Co., Ltd.
• Inventors: Irie, Kota, Takemura, Masayuki, Muramatsu, Shoji, Hayakawa, Yasuhisa, Fukata, Osamu, Kiyohara, Masahiro, Utagawa, Akira
• Primary Examiner(s): Ustaris, Joseph
• Assistant Examiner(s): NAWAZ, TALHA M
• Application Number:
  US14/416,851
• Publication Number:
  US 20150201120A1
• Time in Patent Office: 970 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: B60R 1/002 specially adapted for covering the peripheral part of the vehicle, e.g. for viewing tyres, bumpers or the like integrated in the windows B60R1/001; combined with rear-view mirrors B60R1/08
• CPC Class Codes: G02B 27/0006 with means to keep optical surfaces clean, e.g. by preventing or removing dirt, stains, contamination, condensation G02B1/18 takes precedence; cleaning in general B08B
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/20224 Image subtraction
• CPC Class Codes: G06T 2207/30261 Obstacle
• CPC Class Codes: G06T 7/254 involving subtraction of images
• CPC Class Codes: G06T 7/73 using feature-based methods
• CPC Class Codes: G06V 20/584 of vehicle lights or traffic lights
• CPC Class Codes: G06V 20/588 Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
• CPC Class Codes: G08G 1/16 Anti-collision systems road vehicle drive control systems for predicting or avoiding probable or impending collision otherwise than by control of a particular sub-unit B60W30/08
• CPC Class Codes: G08G 1/166 for active traffic, e.g. moving vehicles, pedestrians, bikes
• CPC Class Codes: G08G 1/167 Driving aids for lane monitoring, lane changing, e.g. blind spot detection
• CPC Class Codes: H04N 23/00 Cameras or camera modules comprising electronic image sensors; Control thereof
• CPC Class Codes: H04N 23/811 by dust removal, e.g. from surfaces of the image sensor or processing of the image signal output by the electronic image sensor

Abstract

An in-vehicle image recognizer effectively detects a moving object from an image even when a lens has grime. In a detection sensitivity adjustor (50) which adjusts detection sensitivity to be increased according to a white turbidity level (U), the detection sensitivity of a vehicle detector (70) (image recognition application execution unit), which detects the other vehicle (6) (moving object) existing in the surrounding area of a vehicle (5) with a predetermined detection sensitivity from the image obtained by an imaging unit (10) disposed in the vehicle (5) to observe the surrounding area of the vehicle (5) through a lens (12) and convert the light signal of the observed surrounding area of the vehicle (5) into an image signal, is corrected based on the attachment level M of the attached matter such as dirt or water drops to the lens (12), which is calculated by an attachment level calculator (26).

Claims

1. An in-vehicle image recognizer, comprising:

an imaging unit which is disposed in a vehicle to observe a surrounding area of the vehicle through a lens, and convert a light signal of the observed surrounding area of the vehicle into an image signal;

an image recognition application execution unit having predetermined detection sensitivity to detect a moving object existing in the surrounding area of the vehicle from the image obtained by the imaging unit;

a white turbidity level calculator which calculates a white turbidity level of the lens from the image signal;

an attachment level calculator which calculates an attachment level of attached matter such as dirt or water drops to the lens; and

a detection sensitivity adjustor which adjusts the detection sensitivity to be increased according to the white turbidity level, wherein

the detection sensitivity adjustor corrects the detection sensitivity based on the attachment level of the attached matter such as the dirt or the water drops to the lens.

2. The in-vehicle image recognizer according to claim 1, wherein the white turbidity level calculator calculates the white turbidity level of the lens based on at least one of an edge intensity distribution and a brightness gradient of the image obtained by the imaging unit.

3. The in-vehicle image recognizer according to claim 1, wherein the detection sensitivity adjustor controls an increase of the detection sensitivity when the attachment level such as the dirt or the water drops to the lens calculated in the attachment level calculator is high.

4. The in-vehicle image recognizer according to claim 1, wherein the detection sensitivity adjustor corrects at least one threshold among a threshold for detecting a pixel having a brightness difference from one image obtained by the imaging unit, a threshold for detecting a pixel having a brightness change in a time-series image obtained at different times by the imaging unit, and a threshold for determining matching when matching the detected pixels having the brightness change from the time-series image obtained at different times by the imaging unit.

5. The in-vehicle image recognizer according to claim 1, wherein the detection sensitivity adjustor corrects at least one of the thresholds, and controls the increase of the detection sensitivity at nighttime compared to daytime when the attachment level of the attached matter such as the dirt or the water drops to the lens and the white turbidity level of the lens are high.

6. The in-vehicle image recognizer according to claim 1, wherein the image recognition application execution unit detects another vehicle close to the vehicle in a backward of the vehicle.

7. The in-vehicle image recognizer according to claim 6, wherein the detection sensitivity adjustor controls the increase of the detection sensitivity when another vehicle is close to the vehicle at a predetermined positive relative speed in the case that the attachment level of the attached matter such as the dirt or the water drops to the lens and the white turbidity level of the lens are high.