CN110926359A - Three-dimensional crack curved surface contour detection method for optimizing two-phase scanning pavement - Google Patents
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Abstract
The invention discloses a three-dimensional crack curved surface contour detection method for optimizing a two-phase scanning road surface, which comprises the steps of firstly, carrying out filtering processing on a road surface three-dimensional image data matrix to obtain a three-dimensional image data matrix I after drying is removed; arranging the filtered three-dimensional data matrix according to rows, and horizontally processing each row of data in the matrix in batches to obtain a data matrix I1Then, each line of data in the matrix is processed vertically in batch to obtain a data matrix I2Then two data matrices I1And I2Are added to obtain a matrix I3(ii) a Filter matrix I3Obtaining information I containing only cracks4A binary image of (a); and finally, carrying out fracture detection qualitative analysis and ROC curve analysis. The method combines curve fitting with smoothing operation, utilizes a smoothing matrix as a sliding window operator to filter a crack data matrix, and simultaneously carries out curve fitting to obtain a PSP curve. The precision of obtaining the curved surface of the road surface can be improved.
Description
Technical Field
The invention belongs to the technical field of road engineering, relates to a method for detecting a road surface, and particularly relates to a method for detecting a three-dimensional crack curved surface profile of an optimized two-phase scanning road surface.
Background
With the vigorous development of the highway industry, the traditional pavement crack two-dimensional detection technology cannot meet the requirements of pavement management systems, intelligent traffic systems and increasingly perfect pavement evaluation technology development, and the pavement three-dimensional detection technology is becoming the development direction of pavement detection. In recent decades, researchers at home and abroad always pursue an efficient and accurate automatic detection and identification method for pavement cracks. With the progress of science and technology and the improvement of the requirements for detecting pavement cracks, improved crack detection algorithms are continuously appeared. In crack detection technologies at home and abroad, a CCD (charge coupled device) camera is mostly adopted to obtain a road surface image, and then the acquired two-dimensional image is subjected to subsequent processing to identify cracks. Due to the limitation of hardware conditions of a two-dimensional image acquisition system and the influence of external illumination, the acquired image has low contrast between cracks and a background, the information amount is small, a large amount of randomly distributed noise, oil stains, tree shadows, black spots and other impurities exist, the crack identification is greatly interfered, the subsequent algorithm has large calculation amount, low accuracy and poor stability, and the processing of a large amount of pavement images is not facilitated. The three-dimensional crack recognition technology is a new field, compared with a two-dimensional image crack recognition technology, the data used by the method is three-dimensional data, and the acquired data is more sufficient and accurate because the acquisition of the three-dimensional data has the remarkable advantage of being not influenced by illumination, oil stains and the like. Therefore, the accuracy and the stability of the three-dimensional crack identification are better. Currently, the three-dimensional crack recognition technology is studied as follows: huang et al propose a multi-scale fracture classification method based on fracture fundamental elements (CFE); tsai et al propose a 3D laser technology-based method for accurately evaluating asphalt pavement cracks under different illumination and low contrast conditions; peng et al propose a three-dimensional road surface image multi-target clustering automatic crack detection method based on 1mm resolution.
The existing three-dimensional detection technology for pavement cracks is to acquire depth information of cracks based on optical three-dimensional information. However, the image-based pavement crack detection methods that have been developed today, either based on two-dimensional images or using captured 3D images, are often less than satisfactory for crack detection results.
Because the precision of the existing three-dimensional crack detection method is relatively low and the high-precision requirement of people on crack collection is met, the research and development of a new pavement three-dimensional crack image detection method for improving the crack detection precision is one of the subjects concerned by the applicant.
Disclosure of Invention
Aiming at the defects or shortcomings existing in the process of collecting data by utilizing laser during vehicle driving, the invention aims to provide a three-dimensional crack curved surface contour detection method for optimizing a two-phase scanning road surface.
In order to realize the task, the invention adopts the following technical solution:
a three-dimensional crack curved surface contour detection method for optimizing a two-phase scanning road surface is characterized in that firstly, a road surface three-dimensional image data matrix is subjected to filtering processing to obtain a three-dimensional image data matrix I after drying is removed; arranging the filtered three-dimensional data matrix I according to rows, and horizontally processing each row of data in the three-dimensional data matrix I in batches to obtain a data matrix I1Then, each line of data in the three-dimensional data matrix I is processed vertically in batches to obtain the data matrix I2Then two data matrices I1And I2Are added to obtain a matrix I3(ii) a Filter matrix I3Obtaining information I containing only cracks4A binary image of (a); and finally, carrying out fracture detection qualitative analysis and ROC curve analysis.
According to the invention, the filter matrix I3The method comprises the following steps:
1) labeling matrix I using 8 neighborhood principle3: for matrix I3Scanning the data points from top to bottom and from left to right, searching unmarked data points, and distributing new marks for the data points; then it is determined whether 8 neighborhoods of each current center point are fracture points. If there is an unmarked crack data point in the region, the crack data point is classified into the region of the current scanning center point, and the mark of the current center point is assigned to the newly found crack data pointCrack data points, updating the center to a new crack point, scanning again and judging 8 neighborhoods of the new center until the data matrix I3All points in (a) are marked;
2) for each marked region, the zero-order distance is first calculated to obtain the area of the region, and then the value of the region is evaluated, the region smaller than the threshold value represents an isolated region which should be regarded as a noise region and is assigned a value of 0 in the filter matrix.
Further, the two data matrixes I1And I2The calculation of the addition is as follows:
calculating a data matrix I1And a data matrix I2Thereby obtaining InInitial matrix of representations I3(ii) a For those points that satisfy the following condition: present in the data matrix I1Or a data matrix I2Is absent from InIn (1), calculating a data matrix I1Or a data matrix I2And InThe shortest distance between the two matrixes is compared with a specified threshold value, and if the shortest distance does not exceed the threshold value, the result is stored in the matrix I3In (1).
The three-dimensional crack curved surface contour detection method for optimizing the two-phase scanning pavement combines curve fitting with smoothing operation, utilizes the smoothing matrix as a sliding window operator to filter a crack data matrix, and simultaneously carries out curve fitting to obtain a PSP curve. The precision of obtaining the curved surface of the road surface can be improved.
Drawings
FIG. 1 is a schematic view of an unbalanced-drive vehicle;
fig. 2 is a graph showing three lateral crack detection results, in which (a) is a 3D display of three-dimensional pavement crack data, (b) is an image obtained by a single two-phase scanning pavement crack detection method, and (c) is an image obtained by a crack detection algorithm based on psp (primary surface profile).
Fig. 3 is a graph of three longitudinal crack detection results, in which (a) is three-dimensional pavement crack data, (b) is an image obtained by a single two-phase scanning pavement crack detection method, and (c) is an image obtained by a crack detection algorithm based on psp (primary Surface profile).
Fig. 4 is a graph of three kinds of mesh crack detection results, in which (a) is three-dimensional road crack data, (b) is an image obtained by a single two-phase scanning road crack detection method, and (c) is an image obtained by a crack detection algorithm based on psp (primary Surface profile).
FIG. 5 is a ROC plot, wherein (a) is a transverse fracture plot, (b) is a longitudinal fracture plot, and (c) is a reticular fracture plot.
The present invention will be described in further detail with reference to the following drawings and examples.
Detailed Description
There are certain problems in using lasers to collect data during vehicle driving. A significant key issue is that the automated pavement performance data collection process may change frequently. These variations are mainly due to two reasons, namely:
(1) unbalanced driving of the vehicle (fig. 1), which will result in a road inclination in the collected data set;
(2) positive and negative noise caused by the laser effect. Specifically, positive noise refers to noise generated on a road by reflected laser light, and then captured by a 3D camera. Negative noise refers to a phenomenon of laser extinction, which often occurs when data is collected in a narrow area (Liet al 2009).
In view of these issues, variations in the data collection process should be considered in developing the crack detection algorithm. Therefore, the applicant designed a crack detection algorithm based on psp (primary Surface profile).
The algorithm can be described as the information containing two main components in the pavement crack image data matrix, namely: background information and fracture information. Typically, the values representing the background information are much higher than the areas of disruption in a certain data matrix. According to this feature, the horizontal direction (or the vertical direction) of the road surface curve can be obtained from the road surface crack image, and then these principal surface profiles are used as a reference for detecting the severity of cracks appearing in the image. After obtaining the PSP curves, calculating the difference between the relative values of the raw data and the corresponding PSP curves may provide actual road surface crack information.
The embodiment provides a three-dimensional crack curved surface contour detection method for optimizing a two-phase scanning road surface, which specifically comprises the following steps:
a first part: image information acquisition and processing
Step 1: acquiring pavement three-dimensional image data by using laser three-dimensional imaging equipment, and filtering the acquired pavement three-dimensional image data matrix to obtain a three-dimensional image data matrix I after drying is removed;
step 2: arranging the filtered three-dimensional data matrix I according to rows, and horizontally processing each row of data in the data matrix I in batches to obtain the data matrix I1；
Step 2.1: using a sliding window of size 3 x 3 on the ith row R of the data matrix IiPerforming smoothing operation (equation 1) to obtain a smoothed row data matrix R'i(formula 2); then, for the smoothed row data matrix R ″iPerforming curve fitting operation to obtain a data matrix R ″i(formula 3); then, a matrix R with the original data is creatediArray Pro of the same sizei(equation 4), the main surface profile data value is stored.
Ri＝(Zi1，Zi2，......，Zin)，i＝1，2，......，m (1)
R′i＝(Z′i1，Z′i2，......，Z′in)，i＝1，2，......，m (2)
R″i＝(Z″i1，Z″i2，......，Z″in)，i＝1，2，......，m (3)
Proi＝(proi1，proi2，......，proin)，i＝1，2，......，m (4)
Where m, n represent the rows and columns of the data matrix, respectively.
Step 2.2: the difference between the smoothed value and the fitted value is calculated using the following equation 5:
wherein, Z'ijAnd Z ″)ijRespectively representing the smoothed data matrix and the fitted data matrix. SiRepresenting the difference between the smoothed data and the fitted data in row i.
Step 2.3: the absolute difference between the smoothed value and the fitted value is calculated using the following equation (6). The significance of Δ is then evaluated using equation (7), and if the value satisfies this criterion, column J is marked and stored into the new array J.
Δ＝|Z′ij-Z″ij| (6)
Δ＞5×Si(7)
Where Δ represents the absolute difference between the smoothed data matrix and the fitted data matrix.
Step 2.4: and the column numbers in the array J are continuously divided and expanded according to the values of the column numbers, and the column numbers in the columns are sorted. The left and right ends extend to the intersection of the curve fitting result and the smoothing result, and the expanded column region is stored in the array J again.
The left and right ends of the column number should satisfy the conditions of formula (8) and formula (9), respectively:
(Z′i(a-1)-Z″i(a-1))×(Z′ia-Z″ia)≤0，a＝1，2，......，m (8)
(Z′ia-Z″ia)×(Z′i(a+1)-Z″i(a+1))≤0，a＝1，2，......，m (9)
wherein, Z'i(a-1)，Z′iaAnd Z'i(a+1)And three neighborhoods, Z ", in the smoothed data matrixi(a-1)，Z″iaAnd Z ″)i(a+1)Representing three neighborhoods in the fitted data matrix;
step 2.5: the data values are assigned to the principal surface profile matrix using equation (10). If a particular column is stored in array J, then the particular value in J should be replaced with a curve fit value, otherwise, the smoothed value is used as the main surface contour value.
Wherein, Z'ij，Z″ijThe smoothed and fitted values are indicated separately.
Step 2.6: calculation of the raw data matrix R using equation (11)iAnd a major surface profile value ProijThe standard deviation between, and is denoted as Ti：
Wherein Z isijIndicating the position of the original data value, proijRepresenting the corresponding position value in the PSP curve.
Step 2.7: r was evaluated using the following formula (12)iThe data of (1). If the result satisfies this condition, the point is marked as a crack and the data value is assigned to 1, otherwise it is considered as background and set to 0.
|Zij-proij|≥k×Ti(12)
Where K represents the parameter identified by the row, and according to the experiments performed herein, the best performance can be obtained when K selects a value of 2 to 5.
Carrying out the same operation on each data point in the data matrix I to obtain a matrix I1The horizontal processing results are shown.
And step 3: operating from the vertical direction (column direction), similarly to step 2, a further data matrix I can be obtained2And vertical information representing image data.
And 4, step 4: two data matrixes I1And I2Are added to obtain a matrix I3。
Step 4.1: calculating a data matrix I1And a data matrix I2Thereby obtaining InInitial matrix of representations I3。
Step 4.2: for those points that satisfy the following condition: exist in I1Or I2In but not storeIs characterized in thatnIn, calculate I1Or I2And InThe shortest distance between them, and comparing the shortest distance with a specified threshold. If the shortest distance does not exceed the threshold, the result is stored in matrix I3In (1).
And 5: the matrix I obtained in the step 43Carrying out filtering operation;
step 5.1: tagging I Using 8 neighborhood principles3For matrix I3The data points in (1) are scanned from top to bottom and from left to right, the data points which are not marked are searched, and new marks are distributed to the data points. Then it is determined whether 8 neighborhoods of each current center point are fracture points. If there is an unmarked fracture data point in the region, the fracture data point is classified into the region where the current scanning center point is located, and the mark of the current center point is assigned to the newly found fracture data point. And updating the center to a new crack point, and scanning again and judging 8 neighborhoods of the new center until the matrix I3All points in (a) are marked.
Step 5.2: for each marked region, the zero-order distance is first calculated, resulting in the area of the region. The value of this region is then evaluated, the region smaller than the threshold representing an isolated region which should be considered as a noise region, and is assigned a value of 0 in the filter matrix.
Step 6: finishing the treatment to obtain the product containing only the crack information I4The binary image of (2).
A second part: qualitative analysis of crack detection results and ROC curve analysis of crack detection results
(1) Qualitative analysis of crack detection results
To evaluate the performance of the proposed crack identification method, three types of pavement crack samples were selected in this example: i.e., transverse fractures, longitudinal fractures, and map fractures. Therefore, a single two-phase scanning pavement crack detection method and a crack detection algorithm based on PSP (Primary Surface Profile) are respectively adopted to carry out comparison tests on all three cracks. The processing results are shown in fig. 2, 3 and 4. Wherein fig. 2(a), 3(a) and 4(a) depict a 3D display of pavement crack data; FIG. 2(b), FIG. 3(b) and FIG. 4(b) depict crack detection results of a single two-phase scanning pavement crack detection method; fig. 2(c), 3(c) and 4(c) depict crack detection results using a psp (primary Surface profile) -based crack detection algorithm.
(2) ROC curve analysis of crack detection results
Another widely used performance evaluation index is the Receiver Operating Characteristic (ROC) curve, which can fully evaluate the performance of the method. ROC is plotted using FPR (false positive rate) as an axis value, and TPR (true positive rate) as an axis value. FPR can be calculated by equation (15), and TPR (also referred to as Recall) can be calculated by equation (14). Generally, as FPR increases, TPR also increases. The closer the TPR is to the value 1, the better the performance of the method. Specifically, the method can be considered to have perfect performance if it can obtain a final TPR value equal to 1 when the FPR becomes 1.
Fig. 5 shows ROC graphs of detection results of a transverse crack (fig. 5a), a longitudinal crack (fig. 5b) and a reticular crack (fig. 5c), which are respectively detected by the three-dimensional crack curved surface profile detection method for the optimized two-phase scanning pavement and the single two-phase scanning pavement crack detection method of the embodiment. The ROC curves show that the three-dimensional crack curved surface contour detection method for optimizing the two-phase scanning pavement provided by the embodiment can improve the detection result of each crack, and particularly, compared with the transverse crack detection, the detection result of each crack is improved obviously for longitudinal cracks and reticular cracks.
TP: predicting the crack (P) and actually predicting the correct crack (T), namely judging the correct rate of the crack;
TN: predicting as a background (N), and actually predicting as correct (T), namely judging as the correct rate of the background;
FP: predicting as a crack (P), actually predicting as an error (F), and judging the background as a crack by a false alarm rate;
FN: the prediction is background (N), actually, the prediction is wrong (F), and the report missing rate refers to the crack judgment as background.
In conclusion, the single two-phase scanning pavement crack detection method can obtain satisfactory detection accuracy, but the recall rate in the experimental result is not satisfactory. The reason for this is to ignore factors of an inclined road surface which can cause variations in the collected data set. The PSP curve is defined as a reference value during the double-phase crack detection, so that the method has stronger anti-interference performance on the change of a data set, and the overall crack detection result is improved. Therefore, the three-dimensional crack curved surface contour detection method for optimizing the two-phase scanning pavement provided by the embodiment has better possibility, and can be integrated into an automatic pavement distress detection system to facilitate the system management of the whole pavement.
Claims (3)
1. A three-dimensional crack curved surface contour detection method for optimizing a two-phase scanning road surface is characterized in that firstly, a road surface three-dimensional image data matrix is subjected to filtering processing to obtain a three-dimensional image data matrix I after drying is removed; arranging the filtered three-dimensional data matrix I according to rows, and horizontally processing each row of data in the three-dimensional data matrix I in batches to obtain a data matrix I1Then, each line of data in the three-dimensional data matrix I is processed vertically in batches to obtain the data matrix I2Then two data matrices I1And I2Are added to obtain a matrix I3(ii) a Filter matrix I3Obtaining information I containing only cracks4A binary image of (a); and finally, carrying out fracture detection qualitative analysis and ROC curve analysis.
2. The method of claim 1, wherein the filter matrix I3The method comprises the following steps:
1) labeling matrix I using 8 neighborhood principle3: for matrix I3Scanning the data points from top to bottom and from left to right, searching unmarked data points, and distributing new marks for the data points; then it is determined whether 8 neighborhoods of each current center point are fracture points. If the unmarked crack data points exist in the region, the crack data points are classified into the region where the current scanning central point is located, the mark of the current central point is distributed to the newly found crack data points, the center is updated to the new crack point, 8 neighborhoods of the new center are scanned again and judged until the data matrix I3All points in (a) are marked;
2) for each marked region, the zero-order distance is first calculated to obtain the area of the region, and then the value of the region is evaluated, the region smaller than the threshold value represents an isolated region which should be regarded as a noise region and is assigned a value of 0 in the filter matrix.
3. The method of claim 1, wherein the two data matrices I1And I2The calculation of the addition is as follows:
calculating a data matrix I1And a data matrix I2Thereby obtaining InInitial matrix of representations I3(ii) a For those points that satisfy the following condition: present in the data matrix I1Or a data matrix I2Is absent from InIn (1), calculating a data matrix I1Or a data matrix I2And InThe shortest distance between the two matrixes is compared with a specified threshold value, and if the shortest distance does not exceed the threshold value, the result is stored in the matrix I3In (1).
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