CN113077426A - Method for detecting defects of clamp plate bolt on line in real time - Google Patents
Method for detecting defects of clamp plate bolt on line in real time Download PDFInfo
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- CN113077426A CN113077426A CN202110317573.7A CN202110317573A CN113077426A CN 113077426 A CN113077426 A CN 113077426A CN 202110317573 A CN202110317573 A CN 202110317573A CN 113077426 A CN113077426 A CN 113077426A
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- 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
- G06T7/0004—Industrial image inspection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30164—Workpiece; Machine component
Abstract
The invention discloses a method for detecting the defects of a splint bolt on line in real time, which comprises the steps of collecting and merging images to obtain a merged atlas; selecting n merging pictures from the merging picture set to form a sample set, and marking a splint area in the merging pictures in the sample set to form a marked picture; all the labeled graphs are sent to a yolov3 network for training to obtain the network weight of yolov 3; sending a detection defect map in the image set to be detected into a deep learning network of yolov3, and predicting an ROI (region of interest) of the splint in the image; cutting out the splint image to obtain a splint image set, and selecting n1 images from the splint image set to obtain a bolt detection image sample set; marking bolt components in the images to obtain a bolt marking image training set, putting the images of the training set into a yolov3 network for training to obtain yolov3 target detection network weight, inputting the marking image set of the bolt to be detected into a yolov3 target detection network, and predicting the ROI of the bolt; and judging the defects of the bolts according to the specific coordinates (x, y) of the bolts.
Description
Technical Field
The invention relates to the field of rail transit image processing, in particular to a method for detecting a splint bolt defect on line in real time.
Background
The importance of the cleat bolts as the primary means of ensuring a smooth connection between the rails is also apparent. However, at present, the control on the safety of the rail is mainly manual detection, and the difficulty of detecting the defects of the clamp bolt is greatly increased due to the particularity of the installation position of the clamp plate, so how to realize the efficient and safe detection of the defects of the clamp bolt is a subject which needs to be researched at present.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method for detecting the defects of a splint bolt on line in real time, which comprises the following steps:
step one, merging 4 images into one image according to the row direction of the acquired image to obtain a merged atlas:
selecting n merging images from the merging image set to form a sample set, marking a splint area in the merging images in the sample set to form a marked image, and forming a to-be-detected image set by the rest merging images;
step three, all the labeled graphs are sent to a yolov3 network for training to obtain a network weight one of yolov 3;
step four, according to the weight one, the detection defect map in the image set to be detected is sent to a deep learning network of yolov3, and the ROI area (x, y, w, h) of the splint in the image to be detected is predicted;
step five, intercepting a splint image according to the position of the splint in the image in the defect image to be detected, obtaining a splint image set, and selecting n1 images from the splint image set to obtain a bolt detection image sample set;
step six, marking bolts to detect bolt components in the images in the image sample set to obtain a bolt mark map, obtaining a bolt mark map training set from the bolt mark map according to a set bolt mark pattern proportion, forming a bolt mark map set to be detected by the rest bolt mark maps, putting images of the training set into a yolov3 network for training to obtain a yolov3 target detection network weight two, inputting the bolt mark map set to be detected into a yolov3 target detection network, and predicting the ROI area of the bolts; and judging the defects of the bolts according to the specific coordinates (x, y) of the bolts.
Furthermore, coordinates P (x, y) of the splints in the image in the defect map to be detected are predicted, the images with 150 x 850 pixels are intercepted by taking P as an initial coordinate, a splint image set is obtained, and n1 images are selected from the splint image set, so that a bolt detection image sample set is obtained.
Further, the detected defect map in the image set to be detected is sent to a deep learning network of yolov3, and the ROI (x, y, w, h) of the splint in the image in the detected defect map is predicted.
Further, the method for judging the defects of the bolts according to the specific positions of the bolts comprises the following steps that 6 bolts are arranged on the clamping plate and are installed at positive and negative intervals, and if the number of the bolts on one side of the clamping plate is less than 3 or the distance between the bolts is not uniform, the bolts are lost on the clamping plate.
The invention has the beneficial effects that: the detection method can accurately detect the splint bolt in real time and judge the defects.
Drawings
FIG. 1 is a schematic diagram of a method for real-time online detection of a splint bolt defect;
FIG. 2 is a schematic diagram of a defect detection process;
fig. 3 is a schematic view of image merging.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
As shown in fig. 1, a method for real-time online detection of a splint bolt defect includes:
step one, merging 4 images into one image according to the row direction of the acquired image to obtain a merged atlas:
selecting n merging images from the merging image set to form a sample set, marking a splint area in the merging images in the sample set to form a marked image, and forming a to-be-detected image set by the rest merging images;
step three, all the labeled graphs are sent to a yolov3 network for training to obtain a network weight one of yolov 3;
step four, according to the weight one, sending the detection defect map in the image set to be detected into a deep learning network of yolov3, and predicting an ROI (region of interest) of a splint in the image to be detected in the defect map, wherein w is the width of the ROI and h is the height of the ROI;
step five, intercepting a splint image according to the position of the splint in the image in the defect image to be detected, obtaining a splint image set, and selecting n1 images from the splint image set to obtain a bolt detection image sample set;
step six, marking bolts to detect bolt components in the images in the image sample set to obtain a bolt mark map, obtaining a bolt mark map training set from the bolt mark map according to a set bolt mark pattern proportion, forming a bolt mark map set to be detected by the rest bolt mark maps, putting images of the training set into a yolov3 network for training to obtain a yolov3 target detection network weight two, inputting the bolt mark map set to be detected into a yolov3 target detection network, and predicting the ROI area of the bolts; and judging the defects of the bolts according to the specific coordinates (x, y) of the bolts.
And predicting coordinates P (x, y) of the splint in the defect image to be detected, taking P as an initial coordinate, taking images of 150-850 pixels to obtain a splint image set, and selecting n1 images from the splint image set to obtain a bolt detection image sample set.
And (3) sending the detection defect map in the image set to be detected into a deep learning network of yolov3, and predicting the ROI (x, y, w, h) of the splint in the image in the defect map to be detected.
The method comprises the following steps that 6 bolts are arranged on the clamping plate and are installed at positive and negative intervals, and if the number of the bolts on one side of the clamping plate is less than 3 or the distance between the bolts is not uniform, the clamping plate is lost.
Specifically, merging of images: taking 4 images and combining the images into one image according to the row direction
1. Selecting 1000 merged pictures as a sample set
2. The area of the splint where the merged image sample is concentrated is marked. All the marked images are sent into yolov3 network for training to obtain the network weight of yolov3,
3. the merged image to be detected is fed into the deep learning network of yolov3 and the specific position of each splint in the image is predicted.
4. And (5) 150 × 850 images are cut at the positions of the clamping plates, and 1000 images are screened out to serve as a bolt detection sample set.
5. The bolt features in the 150 x 850 image are labeled. And putting 1000 images marked by 150 × 850 into yolov3 network for training to obtain the target detection network weight of yolov 3.
6. Inputting the image of 150 x 850 to be detected into yolov3 target detection network, and predicting the concrete position of the bolt.
According to there are 6 bolts on present splint to according to positive and negative spaced installation side, the reasoning is out: if there are fewer than 3 bolts on one side of the clamping plate or the spacing of the bolts is not uniform, there is a bolt missing at the clamping plate.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (4)
1. A method for detecting the defects of a splint bolt on line in real time is characterized by comprising the following steps:
step one, merging 4 images into one image according to the row direction of the acquired image to obtain a merged atlas:
selecting n merging images from the merging image set to form a sample set, marking a splint area in the merging images in the sample set to form a marked image, and forming a to-be-detected image set by the rest merging images;
step three, all the labeled graphs are sent to a yolov3 network for training to obtain a network weight one of yolov 3;
step four, according to the weight one, the detection defect map in the image set to be detected is sent to a deep learning network of yolov3, and the ROI area (x, y, w, h) of the splint in the image to be detected is predicted;
step five, intercepting a splint image according to the position of the splint in the image in the defect image to be detected, obtaining a splint image set, and selecting n1 images from the splint image set to obtain a bolt detection image sample set;
step six, marking bolts to detect bolt components in the images in the image sample set to obtain a bolt mark map, obtaining a bolt mark map training set from the bolt mark map according to a set bolt mark pattern proportion, forming a bolt mark map set to be detected by the rest bolt mark maps, putting images of the training set into a yolov3 network for training to obtain a yolov3 target detection network weight two, inputting the bolt mark map set to be detected into a yolov3 target detection network, and predicting the ROI area of the bolts; and judging the defects of the bolts according to the coordinates (x, y) of the bolts.
2. The method for on-line real-time detection of splint bolt defects according to claim 1, wherein the coordinates P (x, y) of the splint in the image in the defect map to be detected are predicted, the image with 150 x 850 pixels is intercepted by taking P as the initial coordinate to obtain a splint image set, and n1 images are selected from the splint image set to obtain a bolt detection image sample set.
3. The method for detecting the bolt defect of the splint in real time on line according to claim 1, wherein the detected defect map in the image set to be detected is sent into a deep learning network of yolov3, and the ROI area (x, y, w, h) of the splint in the image to be detected is predicted.
4. The method for detecting the bolt defect of the clamping plate on line in real time as claimed in claim 1, wherein the step of judging the defect of the bolt according to the specific position of the bolt comprises the following steps that 6 bolts are arranged on the clamping plate and are installed at positive and negative intervals, and if fewer than 3 bolts are arranged on one side of the clamping plate or the distance between the bolts is not uniform, the bolt defect exists on the clamping plate.
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| CN202110317573.7A CN113077426B (en) | 2021-03-23 | 2021-03-23 | Method for detecting defects of clamp plate bolt on line in real time |
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| CN202110317573.7A CN113077426B (en) | 2021-03-23 | 2021-03-23 | Method for detecting defects of clamp plate bolt on line in real time |
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| CN111080597A (en) * | 2019-12-12 | 2020-04-28 | 西南交通大学 | Track fastener defect identification algorithm based on deep learning |
| CN112149665A (en) * | 2020-09-04 | 2020-12-29 | 浙江工业大学 | High-performance multi-scale target detection method based on deep learning |
| US20210073692A1 (en) * | 2016-06-12 | 2021-03-11 | Green Grid Inc. | Method and system for utility infrastructure condition monitoring, detection and response |
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2021
- 2021-03-23 CN CN202110317573.7A patent/CN113077426B/en active Active
Patent Citations (7)
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| US20210073692A1 (en) * | 2016-06-12 | 2021-03-11 | Green Grid Inc. | Method and system for utility infrastructure condition monitoring, detection and response |
| WO2020052633A1 (en) * | 2018-09-13 | 2020-03-19 | Virtual Control Limited | System and method for determining a condition of an object |
| CN109886102A (en) * | 2019-01-14 | 2019-06-14 | 华中科技大学 | A kind of tumble behavior Spatio-temporal domain detection method based on depth image |
| CN109977817A (en) * | 2019-03-14 | 2019-07-05 | 南京邮电大学 | EMU car bed bolt fault detection method based on deep learning |
| CN110097536A (en) * | 2019-04-10 | 2019-08-06 | 东南大学 | Hexagon bolt looseness detection method based on deep learning and Hough transformation |
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