WO2023005057A1 - 相机盖板清洁度检测方法及检测装置 - Google Patents
相机盖板清洁度检测方法及检测装置 Download PDFInfo
- Publication number
- WO2023005057A1 WO2023005057A1 PCT/CN2021/130123 CN2021130123W WO2023005057A1 WO 2023005057 A1 WO2023005057 A1 WO 2023005057A1 CN 2021130123 W CN2021130123 W CN 2021130123W WO 2023005057 A1 WO2023005057 A1 WO 2023005057A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- image
- ratio
- threshold
- gray
- camera
- Prior art date
Links
- 230000003749 cleanliness Effects 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000012545 processing Methods 0.000 claims abstract description 23
- 238000003384 imaging method Methods 0.000 claims abstract description 19
- 238000001514 detection method Methods 0.000 claims description 50
- 238000011109 contamination Methods 0.000 claims description 21
- 238000005259 measurement Methods 0.000 abstract description 5
- 238000012360 testing method Methods 0.000 description 10
- 238000005192 partition Methods 0.000 description 6
- 230000002159 abnormal effect Effects 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 239000000428 dust Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 125000000205 L-threonino group Chemical group [H]OC(=O)[C@@]([H])(N([H])[*])[C@](C([H])([H])[H])([H])O[H] 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 239000006059 cover glass Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/94—Investigating contamination, e.g. dust
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N17/00—Diagnosis, testing or measuring for television systems or their details
- H04N17/002—Diagnosis, testing or measuring for television systems or their details for television cameras
Definitions
- the invention relates to a camera, in particular to a method and a detection device for detecting the cleanliness of a camera cover plate.
- Time of Flight (TOF) camera is a new generation of distance detection and 3D imaging technology products.
- the distance to the target is obtained by continuously sending light pulses to the target, and then using the imaging sensor to receive the light returned from the object, and detecting the flight (round trip) time of the light pulse.
- the distance to the target is obtained by continuously sending light pulses to the target, and then using the imaging sensor to receive the light returned from the object, and detecting the flight (round trip) time of the light pulse.
- dust will gradually accumulate on the surface of the cover glass, which will cause inaccurate distance measurement. Therefore, it is necessary to detect whether there is dust on the surface of the cover plate and judge its dirty state.
- the patent for invention with the publication number CN 111970506 B discloses a method, device and equipment for lens contamination detection.
- the method includes: obtaining the lens image obtained by shooting the lens to be detected; combining the center point coordinates of the lens image, dividing the image into Two partitions and one total area, combined with the gray value of different areas for partition detection, and comprehensive detection based on the gray value of the total detection area.
- the detection data of the partition and the detection data of the total area are summarized as the overall detection result of the lens to be detected. Detect whether there is dirt on the lens through image partitions, realize automatic detection of lens dirt, avoid detection errors caused by manual identification, and detect more comprehensively, which can improve the reliability of lens dirt detection.
- the object of the present invention is to provide a method and a detection device for detecting the cleanliness of a camera cover plate, so as to solve the problems that the detection method for lens contamination is cumbersome, and it is difficult to detect and judge lens contamination.
- the method for detecting the cleanliness of the camera cover of the present invention comprises the steps of:
- the second image of the target object is acquired by a camera having a cover plate to be detected, and the second image grayscale average value and the second dark corner grayscale average value are obtained according to the second image, and the second dark corner grayscale is obtained.
- S2 Perform a ratio operation on the second ratio and the first ratio to obtain a third ratio, and determine whether the cover plate to be detected is in a dirty state according to a comparison result between the third ratio and the first threshold.
- the reference of detection obtain the second image by the camera with the cover plate to be detected, and obtain the second image gray level average value and the second dark corner gray value average value from the second image, and calculate the second ratio, and finally according to A third ratio is obtained from the second ratio and the first ratio, and according to a comparison result between the third ratio and the first threshold, it is judged whether the cover plate to be detected is in a dirty state.
- the method for detecting the cleanliness of the camera cover of the present invention calculates the average gray value of two images four times, and the number of calculated pixel points is large, which improves the reliability of the detection result of the cleanliness of the camera cover.
- the method for detecting the cleanliness of the camera cover of the present invention has simple and rigorous steps, does not need to frequently set dynamic thresholds, and the cleanliness detection of the camera cover is simple.
- judging whether the cover plate to be detected is in a dirty state according to the comparison result of the third ratio and the first threshold includes the steps of:
- step S3 is also performed, and the step S3 includes:
- the second threshold is greater than the first threshold
- the third threshold is greater than the second threshold
- the degree of contamination of the cover plate to be detected is judged according to a comparison result of the third ratio with the first threshold, the second threshold, and the third threshold.
- the step of judging the degree of contamination of the cover plate to be detected according to the comparison result of the third ratio with the first threshold, the second threshold and the third threshold includes:
- the third ratio is greater than or equal to the third threshold, it is determined that the degree of contamination of the cover plate to be detected is heavily dirty.
- the beneficial effect is that after it is determined that the cover to be detected is in a dirty state, the degree of contamination of the cover to be detected is further judged, so as to improve the accuracy of detection of the cleanliness of the camera cover.
- a laser device for emitting laser pulses to a target object
- the processing module determines that the third ratio is smaller than the first threshold, it is determined that the cover plate of the camera to be detected is in a clean state.
- FIG. 1 is a flow chart of a method for detecting the cleanliness of a camera cover plate according to an embodiment of the present invention
- Fig. 2 is a flowchart of judging whether the cover plate to be detected is in a dirty state according to an embodiment of the present invention
- Fig. 3 is a flow chart of judging the degree of contamination of the cover plate to be detected according to an embodiment of the present invention
- FIG. 4 is a schematic structural view of a detection device according to an embodiment of the present invention.
- FIG. 5 is a schematic diagram of the positions of the camera cover and the laser device according to the embodiment of the present invention.
- FIG. 1 is a flow chart of the method for detecting the cleanliness of a camera cover according to an embodiment of the present invention.
- the method for detecting the cleanliness of the camera cover includes steps:
- S1 Obtain the first image of the target object through a camera with a reference cover, and the state of the reference cover is a clean state, so as to ensure the reliability of detecting the cleanliness of the camera cover.
- the reference of detection obtain the second image by the camera with the cover plate to be detected, and obtain the second image gray level average value and the second dark corner gray value average value from the second image, and calculate the second ratio, and finally according to A third ratio is obtained from the second ratio and the first ratio, and according to a comparison result between the third ratio and the first threshold, it is judged whether the cover plate to be detected is in a dirty state.
- the method for detecting the cleanliness of the camera cover plate of the present invention calculates the gray level average value for four times in two images to obtain four gray level average values, thereby increasing the calculated pixel sample size and improving the cleanliness of the camera cover plate reliability of test results.
- the method for detecting the cleanliness of the camera cover of the present invention has simple and rigorous steps, does not need to frequently set dynamic thresholds, and the cleanliness detection of the camera cover is simple.
- step S1 it also includes positioning the cover plate and the target object so that the target object is located within the farthest measurable distance of the camera.
- the pixels are not clear or the gray value calculation of the pixels is inaccurate, and the obtained pixels are more reliable, so that The result of judging the dirty state of the camera cover is more accurate.
- the farthest measurable distance of the camera is specifically the distance between the laser device of the camera and the target object.
- step S1 the step of obtaining the average gray level of the first image and the average gray level of the first dark corner according to the first image includes:
- the formula for calculating the average gray value of the first image is as follows:
- the gray value sum IR_sum_2 is divided by the second gray value sum IR_sum_2 and the second pixel number Pixels_2 to obtain the first shading average gray value IR_ref.
- the formula for calculating the gray average value of the first vignetting angle is as follows:
- step S1 the step of obtaining the average gray level of the second image and the average gray level of the second vignetting angle according to the second image includes:
- the formula for calculating the average gray value of the first image is as follows:
- IR_test IR_sum_4/Pixels_4.
- FIG. 2 is a flow chart of judging whether the cover plate to be detected is in a dirty state according to the embodiment of the present invention.
- step S2 judging whether the cover plate to be detected is in a dirty state according to the comparison result of the third ratio and the first threshold includes steps:
- the second threshold thres_2 is greater than the first threshold thres_1
- the third threshold thres_3 is greater than the second threshold thres_2, that is, the relationship between the three thresholds is:
- the degree of contamination of the cover plate to be detected is judged.
- FIG. 3 is a flow chart of judging the degree of contamination of the cover plate to be detected according to the embodiment of the present invention.
- the step of judging the degree of contamination of the cover plate to be detected according to the comparison result of the third ratio with the first threshold, the second threshold and the third threshold includes:
- FIG. 4 is a schematic structural diagram of the detection device according to an embodiment of the present invention.
- the detection device includes:
- the processing module 3 is configured to perform a ratio operation on the gray average value of the first vignetting angle and the average gray scale value of the first image to obtain the first ratio, and perform a ratio operation on the gray average value of the second dark corner and the average gray scale value of the second image rationing to obtain a second ratio, and performing a ratio operation on the second ratio to the first ratio to obtain a third ratio;
- the processing module 3 is further configured to judge whether the cover plate of the camera to be detected is in a dirty state according to the comparison result of the third ratio and the first threshold.
- the present invention acquires the first image and the second image, the average gray level of the first image, the average gray level of the first dark corner, the average gray level of the second image, and the average gray level of the second dark corner through the imaging sensor 6;
- the processing module 3 obtains the first ratio, the second ratio and the third ratio, and judges the size of the third ratio and the first threshold through the judging module 4, thereby judging whether the cover plate to be detected is in a dirty state.
- the reliability of the detection result of the cleanliness of the camera cover is improved, and the accuracy of the method for detecting the cleanliness of the camera cover is improved.
- the processing module 3 is also used for positioning the camera cover 2 and the target object 1 according to the distance between the camera cover 2 and the target object 1 so that the target object 1 is within the farthest measurable distance of the camera. Specifically, the actual distance between the laser device 5 and the target object 1 is within the farthest measurable distance range of the camera.
- the processing module 3 of the present invention judges the steps of the state of the camera cover to be detected as follows:
- the processing module 3 determines that the third ratio is smaller than the first threshold, it is determined that the camera cover to be detected is in a clean state
- the processing module 3 judges that the third ratio is greater than or equal to the first threshold, it determines that the camera cover to be detected is in a dirty state, and when the processing module 3 determines that the camera cover is in a dirty state, it also executes to determine the degree of contamination of the camera cover step:
- the processing module 3 determines that the third ratio is greater than or equal to the first threshold and less than the second threshold, it is determined that the camera cover to be detected is in a slightly dirty state
- the processing module 3 determines that the third ratio is greater than or equal to the second threshold and less than the third threshold, it is determined that the camera cover to be detected is in a moderately dirty state
- the detection device of the present invention further includes an alarm device 4 and a control circuit 7 .
- the alarm device 4 is connected with the processing module 3 through the control circuit 7 .
- the control circuit 7 controls the alarm device 4 to generate an alarm signal to remind the user that the camera cover is in a dirty state, and the camera cover needs to be cleaned in time to ensure accurate distance measurement of the camera performance.
- the alarm device is an LED indicator light capable of generating an alarm light signal or an acoustic sound capable of generating an alarm sound.
- FIG. 5 is a schematic diagram of the positions of the camera cover and the laser device according to the embodiment of the present invention.
- the positional relationship between the camera cover 2 , the target object 1 , the laser device 5 and the imaging sensor 6 is shown in FIG. 5 .
- the left side of the camera cover 2 is the inner side of the camera cover 2
- the laser device 5 and the imaging sensor 6 are located on the inner side of the camera cover 2 .
- the right side of the camera cover 2 is the outside of the camera cover 2
- the target object 1 is located outside the camera cover 2 .
- the principle of detecting dirty pixels is as follows:
- the light of the laser pulse emitted by the laser device 5 passing through the dirty area 20 will be reflected back by the dirty area 20, and a part of the laser pulse reflected by the dirty area 20 is transmitted to the dark corner area in the upper left corner and the dark corner area in the upper right corner of the imaging sensor 6.
- the sum of the corner area, the lower left vignetted area, and the lower right vignetted area, the upper left vignetted area, the upper right vignetted area, the lower left vignetted area, and the lower right corner vignetted area is the vignetted area , so that the pixels in the dark corner area in the image become.
- Its light propagation path is L2
- the imaging sensor 6 receives the laser light reflected back by the dirty area 20, and the dark corner area 60 of the imaging sensor 6 receives the reflected laser light.
- the gray value of the pixels in the vignetting area increases significantly, and the larger the dirty area, the more pixels with increased gray value.
- the second ratio of the average gray value of the dark corner detected by the camera to be detected to the average gray value of the entire area of the second image will be significantly greater than the average gray value of the dark corner area detected by the camera for cleaning the cover plate and the average gray value of the first image.
- the ratio of the first ratio, the second ratio, and the first ratio of the gray average value of the entire area is greater than the first threshold, and it is determined that the cover plate to be detected is in a dirty state.
- Vignetting refers to the phenomenon that the captured image becomes darker in the four corner areas.
- the vignetting area of the present invention refers to that the pixels in the four areas of the upper left corner area, the lower left corner area, the upper right corner area and the lower right corner area of the imaging sensor are darker than the pixels of the whole image.
- the vignetting The gray value of the pixels in the area will be lower than the gray value of the image as a whole.
- the camera cover is in a dirty state, a part of the laser light will be reflected by the dirty area to the dark corner area, so that the gray value of the pixels in the dark corner area will increase.
- the area of the vignetting area is related to the occlusion of the mechanical assembly of the camera, so the area of the vignetting area and the ratio of the area of the vignetting area to the entire image are different in the images captured by each camera.
- the area of the vignetting area accounts for 0.8% of the entire first image area or the second image area.
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Multimedia (AREA)
- Chemical & Material Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Biomedical Technology (AREA)
- Theoretical Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Quality & Reliability (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Studio Devices (AREA)
- Camera Bodies And Camera Details Or Accessories (AREA)
Abstract
本发明提供了一种相机盖板清洁度检测方法，包括通过具有参照盖板的相机获取第一图像、第一图像灰度平均值、第一暗角灰度平均值和第一比值；通过具有待检测盖板的相机获取第二图像、第二图像灰度平均值、第二暗角灰度平均值和第二比值，依据第一比值和第二比值获取第三比值，依据第三比值与第一阈值的比较结果判断待检测盖板是否处于脏污状态。提升了计算的像素点样本量，提高了相机盖板的清洁度检测结果的可靠性，提高了相机盖板的清洁度检测结果的可靠性和准确性。本发明检测装置包括激光装置、成像传感器和处理模块，相机盖板的清洁度检测简单。提高了相机盖板的清洁度检测结果的可靠性和准确性。
Description
交叉引用
本申请要求2021年7月30日提交的申请号为2021108747573的中国专利申请的优先权。上述申请的内容以引用方式被包含于此。
本发明涉及相机，尤其涉及一种相机盖板清洁度检测方法及检测装置。
飞行时间相机(Time of Flight，TOF)是新一代距离检测及3D成像技术产品。是通过给目标连续发送光脉冲，然后用成像传感器接收从物体返回的光，通过探测光脉冲的飞行(往返)时间来得到目标物距离。TOF相机在工作过程中，其盖板玻璃表面会逐渐积累灰尘，会造成测距不准的情况。因此，需要检测盖板表面是否存在灰尘，判断其脏污状态。
公开号为CN 111970506 B的发明专利公开了及一种镜头脏污检测方法、装置和设备，该方法包括：获取对待检测镜头拍摄得到的镜头图像；结合镜头图像的中心点坐标，将图像划分成两个分区和一个总区域，结合不同区域的灰度值进行分区检测，以及根据总检测区域的灰度值进行综合检测。通过对分区和包含分区的总检测区域均进行相应的脏污检测，将分区检测数据和总区域的检测数据汇总，作为待检测镜头的整体检测结果。通过图像分区检测镜头是否存在脏污，实现对镜头脏污的自动检测，避免人工识别导致的检测误差，且检测更加全面，能够提高镜头脏污检测的可靠性。该发明先计算图像总区域的灰度平均值，根据所述总检测区域的灰度平均值和方差确定基 准阈值，分别根据所述第一检测分区和所述第二检测分区的灰度数据，通过动态阈值检测所述第一检测分区和所述第二检测分区是否存在脏污，得到所述第一检测分区和所述第二检测分区的脏污检测结果。但是该发明如何划分第一检测分区和第二检测分区是一个难题，如何设置动态阈值检测又是一个难题，使得该镜头脏污检测方法变得繁琐，检测及判断镜头脏污难度高。
因此，有必要提供一种相机盖板清洁度检测方法及检测装置以解决上述的现有技术中存在的问题。
发明概要
本发明的目的在于提供一种相机盖板清洁度检测方法及检测装置，以解决镜头脏污检测方法繁琐，检测及判断镜头脏污难度高的问题。
为实现上述目的，本发明的所述一种相机盖板清洁度检测方法，包括步骤：
S1：通过具有参照盖板的相机获取目标物体的第一图像，依据所述第一图像获取第一图像灰度平均值和第一暗角灰度平均值，对所述第一暗角灰度平均值与所述第一图像灰度平均值执行比值运算以得到第一比值；
通过具有待检测盖板的相机获取所述目标物体的第二图像，依据所述第二图像获取第二图像灰度平均值和第二暗角灰度平均值，对所述第二暗角灰度平均值与所述第二图像灰度平均值执行比值运算以得到第二比值；
S2：对所述第二比值与所述第一比值执行比值运算以得到第三比值，依据所述第三比值与第一阈值的比较结果判断所述待检测盖板是否处于脏污状态。
本发明的所述相机盖板清洁度检测方法的有益效果在于：
通过具有参照盖板的相机获取第一图像，并从第一图像中获取第一图像灰度平均值和第一暗角灰度平均值，并计算得到第一比值，作为相机盖板的清洁度检测的参考；通过具有待检测盖板的相机获取第二图像，并从第二图像中获取第二图像灰度平均值和第二暗角灰度平均值，并计算得到第二比值，最后依据第二比值与第一比值得到第三比值，并依据第三比值与第一阈值的比较结果，判断待检测盖板是否处于脏污状态。本发明的所述相机盖板清洁度检测方法对两个图像进行四次灰度平均值的计算，计算的像素点的样本量多，提高了相机盖板的清洁度检测结果的可靠性。通过计算灰度平均值，从而避免了个别异常像素或异常像素区域的灰度值过大或过小造成的检测结果不准确的情况，提高了所述相机盖板清洁度检测方法的准确性。本发明的所述相机盖板清洁度检测方法步骤简单且严谨，不需要频繁设置动态阈值，相机盖板的清洁度检测简单。
优选地，所述步骤S2中，依据所述第三比值与第一阈值的比较结果判断所述待检测盖板是否处于脏污状态包括步骤：
当判断所述第三比值大于或等于所述第一阈值，判定所述待检测盖板处于脏污状态；
当判断所述第三比值小于所述第一阈值，判定所述待检测盖板处于清洁状态。
进一步优选地，当判定所述待检测盖板处于脏污状态后，还执行步骤S3，所述步骤S3包括：
设置第二阈值和第三阈值，所述第二阈值大于所述第一阈值，所述第三阈值大于所述第二阈值；
依据所述第三比值与所述第一阈值、所述第二阈值和所述第三阈值的比较结果判断所述待检测盖板的脏污程度。
进一步优选地，依据所述第三比值与所述第一阈值、所述第二阈值和所述第三阈值的比较结果判断所述待检测盖板的脏污程度的步骤包括：
当判断所述第三比值大于或等于所述第一阈值且小于所述第二阈值时，判定所述待检测盖板的脏污程度为轻脏；
当判断所述第三比值大于或等于所述第二阈值且小于所述第三阈值时，判定所述待检测盖板的脏污程度为中脏；
当判断所述第三比值大于或等于所述第三阈值，判定所述待检测盖板的脏污程度为重脏。其有益效果在于，判定待检测盖板处于脏污状态后，进一步判断待检测盖板的脏污程度，以提高相机盖板的清洁度检测的精确度。
优选地，所述步骤S1中，依据所述第一图像获取第一图像灰度平均值和第一暗角灰度平均值的步骤包括：
计算所述第一图像内像素点数以得到第一像素点数，并计算所述第一图像内所有像素的灰度值总和以得到第一灰度值总和，对所述第一灰度值总和与所述第一像素点数执行除运算以得到第一图像灰度平均值；
在所述第一图像中确定第一暗角区域，计算所述第一暗角区域内的像素点数以得到第二像素点数，并计算所述第一暗角区域的所有像素的灰度值总和以得到第二灰度值总和，对所述第二灰度值总和与所述第二像素点数执行除运算以得到所述第一暗角灰度平均值。
优选地，所述步骤S1中，依据所述第二图像获取第二图像灰度平均值和第二暗角灰度平均值的步骤包括：
计算所述第二图像内像素点数以得到第三像素点数，并计算所述第二图像内所有像素的灰度值总和以得到第三灰度值总和，对所述第三灰度值总和与所述第三像素点数执行除运算以得到第二图像灰度平均值；
在所述第二图像中确定第二暗角区域，计算所述第二暗角区域内的像素点数以得到第四像素点数，并计算所述第二暗角区域的所有像素的灰度值总和以得到第四灰度值总和，对所述第四灰度值总和与所述第四像素点数执行除运算以得到所述第二暗角灰度平均值。
本发明还提供一种检测装置，其特征在于，包括：
激光装置，用于向目标物体发射激光脉冲；
成像传感器，用于接收所述激光脉冲并形成第一图像和第二图像，并依据所述第一图像获取第一图像灰度平均值和第一暗角灰度平均值，依据所述第二图像获取第二图像灰度平均值和第二暗角灰度平均值；
处理模块，与所述成像传感器连接，用于计算相机盖板与目标物体间的距离，用于对所述第一暗角灰度平均值与所述第一图像灰度平均值执行比值运算以得到第一比值，对所述第二暗角灰度平均值与所述第二图像灰度平均值执行比值运算以得到第二比值，并对所述第二比值与所述第一比值执行比值运算以得到第三比值；
所述处理模块还用于依据所述第三比值与第一阈值的比较结果判断待检测相机盖板是否处于脏污状态。
本发明的所述检测装置的有益效果在于：
本发明通过成像传感器获取第一图像和第二图像，通过成像传感器获取第一图像灰度平均值、第一暗角灰度平均值、第二图像灰度平均值和第二暗 角灰度平均值；通过处理模块获取第一比值、第二比值和第三比值，并判断第三比值与第一阈值的大小，从而依据第三比值与第一阈值的比较结果来判断待检测盖板是否处于脏污状态。提高了相机盖板的清洁度检测结果的可靠性，提高了所述相机盖板清洁度检测方法的准确性。
优选地，当所述处理模块判断所述第三比值大于或等于所述第一阈值，判定所述待检测相机盖板处于脏污状态；
当所述处理模块判断所述第三比值小于所述第一阈值，判定所述待检测相机盖板处于清洁状态。
图1为本发明实施例的相机盖板清洁度检测方法流程图；
图2为本发明实施例的判断待检测盖板是否处于脏污状态的流程图；
图3为本发明实施例的判断待检测盖板的脏污程度的流程图；
图4为本发明实施例的检测装置的结构示意图；
图5为本发明实施例的相机盖板与激光装置的位置示意图。
发明内容
为使本发明实施例的目的、技术方案和优点更加清楚，下面将对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。除非另外定义，此处使用的技术术语或者科学术语应当为本发明所属领域内具有一般技能的人士所理解的通常意义。本文中使用的 “包括”等类似的词语意指出现该词前面的元件或者物件涵盖出现在该词后面列举的元件或者物件及其等同，而不排除其他元件或者物件。
针对现有技术存在的问题，本发明实施例提供了一种相机盖板清洁度检测方法，为实现上述目的，图1为本发明实施例的相机盖板清洁度检测方法流程图。参照图1，相机盖板清洁度检测方法包括步骤：
S1：通过具有参照盖板的相机获取目标物体的第一图像，参照盖板的状态为清洁状态，以保证检测相机盖板的清洁度的可靠性。依据第一图像获取第一图像灰度平均值IR_mean_1和第一暗角灰度平均值IR_ref，对第一暗角灰度平均值IR_ref与第一图像灰度平均值IR_mean执行比值运算以得到第一比值ratio_ref，第一比值的计算公式为：
ratio_ref＝IR_ref/IR_mean_1；
通过具有待检测盖板的相机获取目标物体的第二图像，依据第二图像获取第二图像灰度平均值IR_mean_2和第二暗角灰度平均值IR_test，对第二暗角灰度平均值IR_test与第二图像灰度平均值IR_mean_2执行比值运算以得到第二比值ratio_test，第二比值的计算公式为：
ratio_test＝IR_test/IR_mean_2。
S2：对第二比值ratio_test与第一比值ratio_ref执行比值运算以得到第三比值ratio，第三比值的计算公式为：
ratio＝ratio_test/ratio_ref；
依据第三比值ratio与第一阈值thres的比较结果判断待检测盖板是否处于脏污状态。
本发明的相机盖板清洁度检测方法的有益效果在于：
通过具有参照盖板的相机获取第一图像，并从第一图像中获取第一图像灰度平均值和第一暗角灰度平均值，并计算得到第一比值，作为相机盖板的清洁度检测的参考；通过具有待检测盖板的相机获取第二图像，并从第二图像中获取第二图像灰度平均值和第二暗角灰度平均值，并计算得到第二比值，最后依据第二比值与第一比值得到第三比值，并依据第三比值与第一阈值的比较结果，判断待检测盖板是否处于脏污状态。本发明的相机盖板清洁度检测方法对两个图像中进行了四次灰度平均值的计算得到四个灰度平均值，从而提升了计算的像素点样本量，提高了相机盖板的清洁度检测结果的可靠性。通过计算灰度平均值，从而避免了个别异常像素或异常像素区域的灰度值过大或过小造成的检测结果不准确的情况，提高了相机盖板清洁度检测方法的准确性。本发明的相机盖板清洁度检测方法步骤简单严谨，不需要频繁设置动态阈值，相机盖板的清洁度检测简单。
在步骤S1之前，还包括对盖板和目标物体进行定位，使所述目标物体位于相机的最远可量测距离内。以使相机获取的图像更加清晰，避免出现相机盖板与目标物体过远或过近而造成的像素点不清晰或像素点的灰度值计算不准确的情况，获取的像素点更加可靠，使相机盖板的脏污状态的判断结果准确度更高。
在一些实施方式中，相机的最远可量测距离具体为相机的激光装置与目标物体之间的距离。
作为本发明一种优选的实施方式，步骤S1中，依据第一图像获取第一图像灰度平均值和第一暗角灰度平均值的步骤包括：
计算第一图像内像素点数以得到第一像素点数Pixels_1，并计算第一图 像内所有像素的灰度值总和以得到第一灰度值总和IR_sum_1，对第一灰度值总和IR_sum_1与第一像素点数Pixels_1执行除运算以得到第一图像灰度平均值IR_mean_1。第一图像灰度平均值的计算公式如下：
IR_mean_1＝IR_sum_1/Pixels_1；
在第一图像中确定第一暗角区域，计算第一暗角区域内的像素点数以得到第二像素点数Pixels_2，并计算第一暗角区域的所有像素的灰度值总和以得到第二灰度值总和IR_sum_2，对第二灰度值总和IR_sum_2与第二像素点数Pixels_2执行除运算以得到第一暗角灰度平均值IR_ref。第一暗角灰度平均值的计算公式如下：
IR_ref＝IR_sum_2/Pixels_2。
作为本发明一种优选的实施方式，步骤S1中，依据第二图像获取第二图像灰度平均值和第二暗角灰度平均值的步骤包括：
计算第二图像内像素点数以得到第三像素点数Pixels_3，并计算第二图像内所有像素的灰度值总和以得到第三灰度值总和IR_sum_3，对第三灰度值总和IR_sum_3与第三像素点数Pixels_3执行除运算以得到第二图像灰度平均值IR_mean_2。第一图像灰度平均值的计算公式如下：
IR_mean_2＝IR_sum_3/Pixels_3；
在第二图像中确定第二暗角区域，计算第二暗角区域内的像素点数以得到第四像素点数Pixels_4，并计算第二暗角区域的所有像素的灰度值总和以得到第四灰度值总和IR_sum_4，对第四灰度值总和IR_sum_4与第四像素点数Pixels_4执行除运算以得到第二暗角灰度平均值IR_test。第二暗角灰度平均值的计算公式如下：
IR_test＝IR_sum_4/Pixels_4。
作为本发明一种优选的实施方式，图2为本发明实施例的判断待检测盖板是否处于脏污状态的流程图。参照图2，步骤S2中，依据第三比值与第一阈值的比较结果判断待检测盖板是否处于脏污状态包括步骤：
S20：当判断第三比值ratio大于或等于第一阈值thres_1，即当ratio≥thres_1时，判定待检测盖板处于脏污状态；
S21：当判断第三比值ratio小于第一阈值thres_1，即ratio＜thres_1时，判定待检测盖板处于清洁状态。
作为本发明一种优选的实施方式，当判定待检测盖板处于脏污状态后，还执行步骤S3，步骤S3包括：
设置第二阈值thres_2和第三阈值thres_3，第二阈值thres_2大于第一阈值thres_1，第三阈值thres_3大于第二阈值thres_2，即三个阈值之间的大小关系为：
thres_1<thres_2<thres_3；
依据第三比值ratio与第一阈值thres_1、第二阈值thres_2和第三阈值thres_3的比较结果判断待检测盖板的脏污程度。
作为本发明一种优选的实施方式，图3为本发明实施例的判断待检测盖板的脏污程度的流程图。参照图3，依据第三比值与第一阈值、第二阈值和第三阈值的比较结果判断待检测盖板的脏污程度的步骤包括：
S30：当判断第三比值ratio大于或等于第一阈值thres_1且小于第二阈值thres_2时，即thres_1≤ratio<thres_2时，判定待检测盖板的脏污程度为轻脏；
S31：当判断第三比值ratio大于或等于第二阈值thres_2且小于第三阈值thres_3时，即thres_2≤ratio<thres_3时，判定待检测盖板的脏污程度为中脏；
S32：当判断第三比值ratio大于或等于第三阈值thres_3，即ratio≥thres_3时，判定待检测盖板的脏污程度为重脏。其优点为，判断待检测盖板的脏污程度，以提高相机盖板的清洁度检测的精确度。
在一些实施方式中，第一阈值thres_1的数值可设置为5-10，第二阈值thres_2的数值可设置为10-15，第三阈值thres_3的数值可设置为大于15的数值。
本发明还提供了一种检测装置，图4为本发明实施例的检测装置的结构示意图。参照图4，检测装置包括：
激光装置5，用于向目标物体1发射激光脉冲；
成像传感器6，用于接收目标物体1反射回来的激光脉冲并形成第一图像和第二图像，获取第一图像和第二图像，并依据第一图像获取第一图像灰度平均值和第一暗角灰度平均值，并依据第二图像获取第二图像灰度平均值和第二暗角灰度平均值；
处理模块3，用于对第一暗角灰度平均值与第一图像灰度平均值执行比值运算以得到第一比值，对第二暗角灰度平均值与第二图像灰度平均值执行比值运算以得到第二比值，并对第二比值与第一比值执行比值运算以得到第三比值；
处理模块还3用于依据第三比值与第一阈值的比较结果判断待检测相机盖板是否处于脏污状态。
本发明的检测装置的优点在于：
本发明通过成像传感器6获取第一图像和第二图像、第一图像灰度平均值、第一暗角灰度平均值、第二图像灰度平均值和第二暗角灰度平均值；通过处理模块3获取第一比值、第二比值和第三比值，通过判断模块4判断第三比值与第一阈值的大小，从而依据第三比值与第一阈值的比较结果来判断待检测盖板是否处于脏污状态。提高了相机盖板的清洁度检测结果的可靠性，提高了相机盖板清洁度检测方法的准确性。
处理模块3还用于依据相机盖板2和目标物体1间的距离对相机盖板和目标物体进行定位，使得目标物体1在相机的最远可量测距离内。具体为激光装置5与目标物体1之间的实际距离在相机的最远可量测距离范围内。
本发明的处理模块3判断待检测相机盖板的状态的步骤如下：
当处理模块3判断第三比值小于第一阈值，判定待检测相机盖板处于清洁状态；
当处理模块3判断第三比值大于或等于第一阈值，判定待检测相机盖板处于脏污状态，当处理模块3判定相机盖板处于脏污状态后，还执行判断相机盖板的脏污程度步骤：
当处理模块3判定第三比值大于或等于第一阈值且小于第二阈值，判定待检测相机盖板处于轻度脏污状态；
当处理模块3判定第三比值大于或等于第二阈值且小于第三阈值，判定待检测相机盖板处于中度脏污状态；
当处理模块3判定第三比值大于或等于第三阈值，判定待检测相机盖板处于重度脏污状态。
为实现检测相机盖板清洁度后的报警或提示功能，本发明的检测装置还包括报警装置4和控制电路7。报警装置4通过控制电路7与处理模块3连接。当处理模块3判定相机盖板处于脏污状态时，控制电路7控制报警装置4产生报警信号，以提示用户相机盖板处于脏污状态，需及时清理相机盖板，以保证相机的准确测距性能。
在一些实施方式中，报警装置为能产生报警光信号的LED提示灯或能产生报警提示音的音响。
在本发明一种具体的实施方式中，图5为本发明实施例的相机盖板与激光装置的位置示意图。参照图5，相机盖板2、目标物体1、激光装置5和成像传感器6的位置关系如图5。相机盖板2的左侧为相机盖板2的内侧，激光装置5和成像传感器6均位于相机盖板2的内侧。相机盖板2的右侧为相机盖板2的外侧，目标物体1位于相机盖板2的外侧。检测脏污像素点的原理如下：
参照图5，当相机盖板2上有脏污区域20时，激光装置5发出的激光脉冲未经过脏污区域20时候，直接穿过相机盖板2而射到目标物体1上，经过目标物体1的反射再穿回相机盖板2内从而射到成像传感器6上，其光线传播路径为L1，在成像传感器6接收经目标物体1反射回来的激光；
激光装置5发出的激光脉冲经过脏污区域20的光线会被脏污区域20反射回来，一部分被脏污区域20反射的激光脉冲传输至成像传感器6的左上角的暗角区域、右上角的暗角区域、左下角的暗角区域和右下角的暗角区域，左上角的暗角区域、右上角的暗角区域、左下角的暗角区域和右下角的暗角区域的总和为暗角区域，从而在成为图像中的暗角区域的像素。其光线传播 路径为L2，成像传感器6接收经脏污区域20反射回来的激光，成像传感器6的暗角区域60接收反射的激光。
因此在相机盖板2上有脏污时，暗角区域内的像素点灰度值明显增大，且脏污面积越大，灰度值增大的像素点越多。待检测盖板相机检测的暗角灰度平均值与第二图像整体区域的灰度平均值的第二比值，会明显大于清洁盖板相机检测的暗角区域灰度值平均值与第一图像整体区域的灰度平均值的第一比值，第二比值和第一比值的比值大于第一阈值，判定待检测盖板处于脏污状态。
暗角是指拍摄的图像在四角区域会变暗的现象。本发明的暗角区域是指在成像传感器的左上角区域、左下角区域、右上角区域和右下角区域四个区域的像素比图像整体的像素暗，在相机盖板处于清洁状态时，暗角区域的像素点的灰度值会比图像整体的灰度值低。在相机盖板处于脏污状态时，一部分激光会被脏污区域反射至暗角区域，从而使暗角区域的像素点的灰度值增大。
可说明的是，暗角区域的面积和相机的机械装配的遮挡有关，因此每个相机的拍摄的图像的暗角区域面积及暗角区域相对于整个图像的面积占比均不相同。
在一些实施方式中，暗角区域的面积占整个第一图像面积或第二图像面积的0.8％。
虽然在上文中详细说明了本发明的实施方式，但是对于本领域的技术人员来说显而易见的是，能够对这些实施方式进行各种修改和变化。但是，应理解，这种修改和变化都属于权利要求书中所述的本发明的范围和精神之 内。而且，在此说明的本发明可有其它的实施方式，并且可通过多种方式实施或实现。
Claims (8)
- 一种相机盖板清洁度检测方法，其特征在于，包括步骤：S1：通过具有参照盖板的相机获取目标物体的第一图像，依据所述第一图像获取第一图像灰度平均值和第一暗角灰度平均值，对所述第一暗角灰度平均值与所述第一图像灰度平均值执行比值运算以得到第一比值；通过具有待检测盖板的相机获取所述目标物体的第二图像，依据所述第二图像获取第二图像灰度平均值和第二暗角灰度平均值，对所述第二暗角灰度平均值与所述第二图像灰度平均值执行比值运算以得到第二比值；S2：对所述第二比值与所述第一比值执行比值运算以得到第三比值，依据所述第三比值与第一阈值的比较结果判断所述待检测盖板是否处于脏污状态。
- 如权利要求1所述的相机盖板清洁度检测方法，其特征在于，所述步骤S2中，依据所述第三比值与第一阈值的比较结果判断所述待检测盖板是否处于脏污状态包括步骤：当判断所述第三比值大于或等于所述第一阈值，判定所述待检测盖板处于脏污状态；当判断所述第三比值小于所述第一阈值，判定所述待检测盖板处于清洁状态。
- 如权利要求2所述的相机盖板清洁度检测方法，其特征在于，当判定所述待检测盖板处于脏污状态后，还执行步骤S3，所述步骤S3包括：设置第二阈值和第三阈值，所述第一阈值大于所述第一阈值，所述第二阈值大于所述第一阈值，所述第三阈值大于所述第二阈值；依据所述第三比值与所述第一阈值、所述第二阈值和所述第三阈值的比较结果判断所述待检测盖板的脏污程度。
- 如权利要求3所述的相机盖板清洁度检测方法，其特征在于，依据所 述第三比值与所述第一阈值、所述第二阈值和所述第三阈值的比较结果判断所述待检测盖板的脏污程度的步骤包括：当判断所述第三比值大于或等于所述第一阈值且小于所述第二阈值时，判定所述待检测盖板的脏污程度为轻脏；当判断所述第三比值大于或等于所述第二阈值且小于所述第三阈值时，判定所述待检测盖板的脏污程度为中脏；当判断所述第三比值大于或等于所述第三阈值，判定所述待检测盖板的脏污程度为重脏。
- 如权利要求1所述的相机盖板清洁度检测方法，其特征在于，所述步骤S1中，依据所述第一图像获取第一图像灰度平均值和第一暗角灰度平均值的步骤包括：计算所述第一图像内像素点数以得到第一像素点数，并计算所述第一图像内所有像素的灰度值总和以得到第一灰度值总和，对所述第一灰度值总和与所述第一像素点数执行除运算以得到第一图像灰度平均值；在所述第一图像中确定第一暗角区域，计算所述第一暗角区域内的像素点数以得到第二像素点数，并计算所述第一暗角区域的所有像素的灰度值总和以得到第二灰度值总和，对所述第二灰度值总和与所述第二像素点数执行除运算以得到所述第一暗角灰度平均值。
- 如权利要求1所述的相机盖板清洁度检测方法，其特征在于，所述步骤S1中，依据所述第二图像获取第二图像灰度平均值和第二暗角灰度平均值的步骤包括：计算所述第二图像内像素点数以得到第三像素点数，并计算所述第二图像内所有像素的灰度值总和以得到第三灰度值总和，对所述第三灰度值总和与所述第三像素点数执行除运算以得到第二图像灰度平均值；在所述第二图像中确定第二暗角区域，计算所述第二暗角区域内的像素点数以得到第四像素点数，并计算所述第二暗角区域的所有像素的灰度值总和以得到第四灰度值总和，对所述第四灰度值总和与所述第四像素点数执行 除运算以得到所述第二暗角灰度平均值。
- 一种检测装置，其特征在于，其特征在于，包括：激光装置，用于向目标物体发射激光脉冲；成像传感器，用于接收所述激光脉冲并形成第一图像和第二图像，并依据所述第一图像获取第一图像灰度平均值和第一暗角灰度平均值，依据所述第二图像获取第二图像灰度平均值和第二暗角灰度平均值；处理模块，与所述成像传感器连接，用于计算相机盖板与目标物体间的距离，用于对所述第一暗角灰度平均值与所述第一图像灰度平均值执行比值运算以得到第一比值，对所述第二暗角灰度平均值与所述第二图像灰度平均值执行比值运算以得到第二比值，并对所述第二比值与所述第一比值执行比值运算以得到第三比值；所述处理模块还用于依据所述第三比值与第一阈值的比较结果判断待检测相机盖板是否处于脏污状态。
- 如权利要求7所述的检测装置，其特征在于，当所述处理模块判断所述第三比值大于或等于所述第一阈值，判定所述待检测相机盖板处于脏污状态；当所述处理模块判断所述第三比值小于所述第一阈值，判定所述待检测相机盖板处于清洁状态。
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110874757.3 | 2021-07-30 | ||
| CN202110874757.3A CN113570582B (zh) | 2021-07-30 | 2021-07-30 | 相机盖板清洁度检测方法及检测装置 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| WO2023005057A1 true WO2023005057A1 (zh) | 2023-02-02 |
Family
ID=78169664
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/CN2021/130123 WO2023005057A1 (zh) | 2021-07-30 | 2021-11-11 | 相机盖板清洁度检测方法及检测装置 |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN113570582B (zh) |
| WO (1) | WO2023005057A1 (zh) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117191809A (zh) * | 2023-08-30 | 2023-12-08 | 宿州绍宸智能科技有限公司 | 基于数据分析的玻璃检测设备故障监测预警系统 |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113570582B (zh) * | 2021-07-30 | 2022-07-29 | 上海集成电路制造创新中心有限公司 | 相机盖板清洁度检测方法及检测装置 |
| CN115870815B (zh) * | 2022-12-28 | 2023-06-16 | 中山超精科技有限公司 | 镜片抛光检测方法及装置 |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080247634A1 (en) * | 2007-04-04 | 2008-10-09 | Hon Hai Precision Industry Co., Ltd. | System and method for detecting defects in camera modules |
| CN106534841A (zh) * | 2016-11-10 | 2017-03-22 | 宁波舜宇车载光学技术有限公司 | 镜头检测方法和装置 |
| CN111970506A (zh) * | 2020-10-20 | 2020-11-20 | 常州市瑞泰光电有限公司 | 镜头脏污检测方法、装置和设备 |
| CN112702488A (zh) * | 2020-12-24 | 2021-04-23 | 上海炬佑智能科技有限公司 | 测试结构件、ToF装置以及镜头脏污检测方法 |
| CN113570582A (zh) * | 2021-07-30 | 2021-10-29 | 上海集成电路制造创新中心有限公司 | 相机盖板清洁度检测方法及检测装置 |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001052185A (ja) * | 1999-08-13 | 2001-02-23 | Mitsubishi Precision Co Ltd | 車両検出方法および車両検出装置 |
| CN106412573B (zh) * | 2016-10-26 | 2019-02-15 | 歌尔科技有限公司 | 一种检测镜头污点的方法和装置 |
| CN106851264B (zh) * | 2017-04-08 | 2020-04-28 | 广东弘景光电科技股份有限公司 | 摄像模组镜头表面检测方法及装置 |
| CN107295270B (zh) * | 2017-07-31 | 2020-01-24 | 爱博康电子(深圳)有限公司 | 一种图像亮度值的确定方法、装置、终端和计算机可读存储介质 |
| CN110738629A (zh) * | 2018-07-02 | 2020-01-31 | 中兴通讯股份有限公司 | 一种镜头脏污的检测方法、终端及计算机可读存储介质 |
| CN111724383A (zh) * | 2020-06-30 | 2020-09-29 | 重庆盛泰光电有限公司 | 基于转盘的摄像头模组黑斑检测系统 |
| CN111739012A (zh) * | 2020-06-30 | 2020-10-02 | 重庆盛泰光电有限公司 | 基于转盘的摄像头模组白斑检测系统 |
| CN112583999B (zh) * | 2020-12-02 | 2024-03-15 | 广州立景创新科技有限公司 | 相机模块的镜头脏污检测方法 |
| CN112862832B (zh) * | 2020-12-31 | 2022-07-12 | 盛泰光电科技股份有限公司 | 一种基于同心圆分割定位的脏污检测方法 |
-
2021
- 2021-07-30 CN CN202110874757.3A patent/CN113570582B/zh active Active
- 2021-11-11 WO PCT/CN2021/130123 patent/WO2023005057A1/zh unknown
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080247634A1 (en) * | 2007-04-04 | 2008-10-09 | Hon Hai Precision Industry Co., Ltd. | System and method for detecting defects in camera modules |
| CN106534841A (zh) * | 2016-11-10 | 2017-03-22 | 宁波舜宇车载光学技术有限公司 | 镜头检测方法和装置 |
| CN111970506A (zh) * | 2020-10-20 | 2020-11-20 | 常州市瑞泰光电有限公司 | 镜头脏污检测方法、装置和设备 |
| CN112702488A (zh) * | 2020-12-24 | 2021-04-23 | 上海炬佑智能科技有限公司 | 测试结构件、ToF装置以及镜头脏污检测方法 |
| CN113570582A (zh) * | 2021-07-30 | 2021-10-29 | 上海集成电路制造创新中心有限公司 | 相机盖板清洁度检测方法及检测装置 |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117191809A (zh) * | 2023-08-30 | 2023-12-08 | 宿州绍宸智能科技有限公司 | 基于数据分析的玻璃检测设备故障监测预警系统 |
| CN117191809B (zh) * | 2023-08-30 | 2024-03-22 | 宿州绍宸智能科技有限公司 | 基于数据分析的玻璃检测设备故障监测预警系统 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113570582A (zh) | 2021-10-29 |
| CN113570582B (zh) | 2022-07-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2023005057A1 (zh) | 相机盖板清洁度检测方法及检测装置 | |
| WO2023005058A1 (zh) | 相机盖板的脏污检测方法及检测装置 | |
| US20230296373A1 (en) | Three-dimensional measurement device | |
| JP7477715B2 (ja) | ライダセンサの光学的クロストークを測定するための方法、およびライダセンサ | |
| JP3452794B2 (ja) | 視程測定装置 | |
| US8215152B2 (en) | Testing an acoustic property of an ultrasound probe | |
| JPWO2021046547A5 (zh) | ||
| CN108444940A (zh) | 基于太赫兹波检测大米的方法及系统 | |
| JPWO2019244946A1 (ja) | 欠陥判別方法、欠陥判別装置、欠陥判別プログラム及び記録媒体 | |
| CN110119208A (zh) | 悬浮显示成像装置及悬浮显示触控方法 | |
| WO2019026457A1 (ja) | 画像監視装置、画像監視方法、画像監視プログラムおよび記録媒体 | |
| JP7402160B2 (ja) | 距離測定装置および信頼性判定方法 | |
| US20190051005A1 (en) | Image depth sensing method and image depth sensing apparatus | |
| TWI590196B (zh) | 液面監測方法 | |
| JPH08285559A (ja) | 表面欠陥検査装置 | |
| JP2000321039A (ja) | 塗装欠陥検査装置及び方法 | |
| JP4534877B2 (ja) | 光学式センサ装置 | |
| US20230138331A1 (en) | Motion in images used in a visual inspection process | |
| JP2021085659A (ja) | 検査装置及び検査方法 | |
| JP3044961B2 (ja) | 円形容器内面検査装置 | |
| KR20160148735A (ko) | 카메라 광중심 측정을 위한 영상 처리 장치 및 그 방법 | |
| CN116576806B (zh) | 一种基于视觉分析的厚度检测设备用的精密度管控系统 | |
| JPH09304364A (ja) | 被検査体傷判定方法及び装置 | |
| JP2008261642A (ja) | シート付着微粒子の検出装置 | |
| CN110543261B (zh) | 一种判断红外触摸屏真实触摸或悬浮的方法 |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| 121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 21951629 Country of ref document: EP Kind code of ref document: A1 |
|
| NENP | Non-entry into the national phase |
Ref country code: DE |