Method and system for detecting large-diameter straight-slit double-sided submerged arc welding steel pipe

Abstract

The invention provides a large-diameter straight seam double-face submerged arc welding steel pipe detection method and system, and belongs to the technical field of industrial image processing. Heat source images of the same position of a steel pipe at different times are obtained through two infrared thermal image sensors, a target area is determined through the image processing and analysis technology, and the welding quality is judged based on the characteristics of the target area; and real-time monitoring and quality detection of the welding process are realized. Images can be obtained and analyzed in real time in the welding process, welding quality flaws are found in time according to changes of the surface temperature, an operator can conveniently adjust welding parameters in time, and the welding quality is improved.

Description

Technical Field

[0001] The present application relates to the technical field of industrial image processing, and in particular to a method and system for detecting large-diameter straight seam double-sided submerged arc welded steel pipes. Background Art

[0002] Large-diameter, double-sided, straight seam submerged arc welded steel pipes are widely used in numerous fields, including oil, natural gas, and water conservancy. The quality of their welding is directly related to the safety and reliability of the project. During the welding process, various defects such as cracks, lack of fusion, and porosity are prone to occur. If these defects are not detected in a timely manner, they may cause serious safety accidents during subsequent use.

[0003] When the local temperature rises too slowly, the gases in the molten pool cannot escape in time and are trapped by the solidifying metal, forming pores. These defects form within the material and cannot be directly identified. Summary of the Invention

[0004] The embodiments of the present application provide a method and system for detecting large-diameter straight seam double-sided submerged arc welded steel pipes to improve the above-mentioned problems.

[0005] To achieve the above objectives, this application adopts the following technical solutions: In the first aspect, the present application proposes a method for detecting large-diameter straight seam double-sided submerged arc welded steel pipes. The method is applicable to a steel pipe welding system, which includes a first infrared thermal imaging sensor, a second infrared thermal imaging sensor, a welding device, and a control terminal. The welding device moves along a first direction in which the steel pipe extends and welds one side surface of the steel pipe. The welding device, the first infrared thermal imaging sensor, and the second infrared thermal imaging sensor are sequentially collinearly arranged in a second direction opposite to the first direction. The method is applicable to the control terminal and includes: A first heat source image is acquired based on a first infrared thermal imaging sensor, and a second heat source image is acquired based on a second infrared sensor, wherein the first heat source image and the second heat source image are located at the same position on the steel pipe; Based on the first heat source image and the second heat source image, determining a target area from the second heat source image; Based on the regional characteristics of the target area, it is determined whether quality defects occur in the steel pipe during the welding process.

[0006] In combination with the first aspect, optionally, obtaining a first heat source image based on a first infrared thermal imaging sensor and obtaining a second heat source image based on a second infrared sensor, wherein the first heat source image and the second heat source image are at the same position on the steel pipe, includes: A first video acquired based on the first infrared thermal imaging sensor and a second video acquired based on the second infrared thermal imaging sensor; Obtaining the spacing between the first infrared thermal imaging sensor and the second infrared thermal imaging sensor, and the welding speed of the welding device when welding along the steel pipe; A first heat source image and a second heat source image are determined based on the first video, the second video, the spacing distance, and the welding speed.

[0007] In combination with the first aspect, optionally, determining the first heat source image and the second heat source image based on the first video, the second video, the interval distance, and the welding speed includes: The M-th frame image is determined from the first video as the first heat source image, and the N-th frame image is determined from the second video as the second heat source image, wherein the shooting time corresponding to the M-th frame image and the shooting time corresponding to the N-th frame image are different from each other and are equal to the interval time divided by the welding speed.

[0008] In combination with the first aspect, optionally, based on the first heat source image and the second heat source image, determining the target area from the second heat source image includes: determining a predicted heat source image based on the first heat source image; The predicted heat source image is compared with the second heat source image, and based on the comparison result, the target area is determined from the second heat source image.

[0009] In combination with the first aspect, optionally, determining the predicted image based on the first heat source image includes: Slicing the first heat source image to form a plurality of sub-heat source images, and determining temperature data corresponding to each sub-heat source image based on the first heat source image; Acquire a steel pipe thermal diffusion model, and based on position information of each sub-heat source image, correspond each sub-heat source image to a plurality of target volume areas on the steel pipe thermal diffusion model; Based on the steel pipe thermal diffusion model, the target thermal diffusion model is obtained; determining second temperature data corresponding to a plurality of sub-heat source images based on second temperature data corresponding to a plurality of target volume regions in a target thermal diffusion model; A predicted heat source image is determined based on the plurality of second temperature data.

[0010] In combination with the first aspect, optionally, comparing the predicted heat source image with the second heat source image, and determining the target area from the second heat source image based on the comparison result, includes: Segmenting the second heat source image to form a plurality of sub-target images, wherein the plurality of sub-target images correspond to the plurality of sub-heat source images; Each sub-target image is compared with the sub-heat source image, and an area consisting of sub-target images different from the corresponding sub-heat source image among the multiple sub-target images is determined as the target area.

[0011] In combination with the first aspect, optionally, based on the first heat source image and the second heat source image, determining the target area from the second heat source image includes: Slicing the first heat source image to form a plurality of sub-heat source images, and determining temperature data corresponding to each sub-heat source image based on the first heat source image; Segmenting the second heat source image to form a plurality of sub-target images, the plurality of sub-target images corresponding to the plurality of sub-heat source images, and determining target temperature data corresponding to each sub-target image based on the second heat source image; Based on the correspondence between the sub-target images and the sub-heat source images, determining the temperature difference between each pair of corresponding sub-target images and sub-heat source images; Based on the position information of each sub-target image, determining the temperature difference between each pair of corresponding sub-target images and sub-heat source images and the difference between the temperature differences of all adjacent pairs of sub-target images and sub-heat source images; Obtain a preset difference value, and compare the preset difference value with the temperature difference value. If the temperature difference between a pair of sub-target images and a sub-heat source image is greater than the temperature difference between two adjacent pairs of sub-target images and sub-heat source images, then determine that the sub-target image is a target area boundary sub-image; Based on the position information of the target area boundary sub-image in the second heat source image, the corresponding boundary of the target area is fitted, and the target area is determined.

[0012] In combination with the first aspect, optionally, determining whether quality defects occur in the steel pipe during welding based on regional characteristics of the target area includes: Obtaining regional features of the target area, the regional features including the area of ​​the target area, the temperature data within the target area, and the shape of the target area; Obtaining a preset area threshold, a preset temperature range, and a preset shape parameter range; The regional features are compared with a preset area threshold, a preset temperature range, and a preset shape parameter range, and whether the steel pipe has quality defects is determined based on the comparison results.

[0013] In a second aspect, the present application proposes a large-diameter straight seam double-sided submerged arc welded steel pipe detection system, comprising a first infrared thermal imaging sensor, a second infrared thermal imaging sensor, a welding device, and a control terminal. The welding device moves along a first direction in which the steel pipe extends and welds one side surface of the steel pipe. The welding device, the first infrared thermal imaging sensor, and the second infrared thermal imaging sensor are sequentially collinearly arranged in a second direction opposite to the first direction. The system is configured as follows: A first heat source image is acquired based on a first infrared thermal imaging sensor, and a second heat source image is acquired based on a second infrared sensor, wherein the first heat source image and the second heat source image are located at the same position on the steel pipe; Based on the first heat source image and the second heat source image, determining a target area from the second heat source image; Based on the regional characteristics of the target area, it is determined whether quality defects occur in the steel pipe during the welding process.

[0014] Optionally, the system is configured to: A first heat source image is acquired based on a first infrared thermal imaging sensor, and a second heat source image is acquired based on a second infrared sensor, where the first heat source image and the second heat source image are located at the same position on the steel pipe, including: A first video acquired based on the first infrared thermal imaging sensor and a second video acquired based on the second infrared thermal imaging sensor; Obtaining the spacing between the first infrared thermal imaging sensor and the second infrared thermal imaging sensor, and the welding speed of the welding device when welding along the steel pipe; A first heat source image and a second heat source image are determined based on the first video, the second video, the spacing distance, and the welding speed.

[0015] Optionally, the system is configured to: Determining a first heat source image and a second heat source image based on the first video, the second video, the interval distance, and the welding speed includes: The M-th frame image is determined from the first video as the first heat source image, and the N-th frame image is determined from the second video as the second heat source image, wherein the shooting time corresponding to the M-th frame image and the shooting time corresponding to the N-th frame image are different from each other and are equal to the interval time divided by the welding speed.

[0016] Optionally, the system is configured to: Determining a target area from the second heat source image based on the first heat source image and the second heat source image includes: determining a predicted heat source image based on the first heat source image; The predicted heat source image is compared with the second heat source image, and based on the comparison result, the target area is determined from the second heat source image.

[0017] Optionally, the system is configured to: Determining a predicted image based on the first heat source image includes: Segmenting the first heat source image to form a plurality of sub-heat source images, and determining temperature data corresponding to each sub-heat source image based on the first heat source image; Acquire a steel pipe thermal diffusion model, and based on position information of each sub-heat source image, correspond each sub-heat source image to a plurality of target volume areas on the steel pipe thermal diffusion model; Based on the steel pipe thermal diffusion model, the target thermal diffusion model is obtained; determining second temperature data corresponding to a plurality of sub-heat source images based on second temperature data corresponding to a plurality of target volume regions in a target thermal diffusion model; A predicted heat source image is determined based on the plurality of second temperature data.

[0018] Optionally, the system is configured to: Comparing the predicted heat source image with the second heat source image, and determining a target area from the second heat source image based on a comparison result, including: Segmenting the second heat source image to form a plurality of sub-target images, wherein the plurality of sub-target images correspond to the plurality of sub-heat source images; Each sub-target image is compared with the sub-heat source image, and an area consisting of sub-target images different from the corresponding sub-heat source image among the multiple sub-target images is determined as the target area.

[0019] Optionally, the system is configured to: Determining a target area from the second heat source image based on the first heat source image and the second heat source image includes: Slicing the first heat source image to form a plurality of sub-heat source images, and determining temperature data corresponding to each sub-heat source image based on the first heat source image; Segmenting the second heat source image to form a plurality of sub-target images, the plurality of sub-target images corresponding to the plurality of sub-heat source images, and determining target temperature data corresponding to each sub-target image based on the second heat source image; Based on the correspondence between the sub-target images and the sub-heat source images, determining the temperature difference between each pair of corresponding sub-target images and sub-heat source images; Based on the position information of each sub-target image, determining the temperature difference between each pair of corresponding sub-target images and sub-heat source images and the difference between the temperature differences of all adjacent pairs of sub-target images and sub-heat source images; Obtain a preset difference value, and compare the preset difference value with the temperature difference value. If the temperature difference between a pair of sub-target images and a sub-heat source image is greater than the temperature difference between two adjacent pairs of sub-target images and sub-heat source images, then determine that the sub-target image is a target area boundary sub-image; Based on the position information of the target area boundary sub-image in the second heat source image, the corresponding boundary of the target area is fitted, and the target area is determined.

[0020] Optionally, the system is configured to: Based on the regional characteristics of the target area, determine whether the steel pipe has quality defects during the welding process, including: Obtaining regional features of the target area, the regional features including the area of ​​the target area, the temperature data within the target area, and the shape of the target area; Obtaining a preset area threshold, a preset temperature range, and a preset shape parameter range; The regional features are compared with a preset area threshold, a preset temperature range, and a preset shape parameter range, and whether the steel pipe has quality defects is determined based on the comparison results.

[0021] According to a third aspect of an embodiment of the present invention, an electronic device is provided, including: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method proposed in the first aspect of the embodiment of the present invention.

[0022] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in the first aspect of the embodiment of the present invention.

[0023] In summary, the above method and system have the following technical effects: The large-diameter, double-sided, submerged arc welded steel pipe inspection method and system provided in this application uses two infrared thermal imaging sensors to capture heat source images of the same location on the pipe at different times. It then uses image processing and analysis techniques to identify the target area and, based on the characteristics of the target area, to determine weld quality. This enables real-time monitoring and quality inspection of the welding process. The system can capture and analyze images in real time during the welding process, promptly identifying weld defects based on surface temperature changes, allowing operators to adjust welding parameters and improve welding quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of a method for inspecting large-diameter straight seam double-sided submerged arc welded steel pipes proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0026] The present application provides a method for detecting large-diameter straight seam double-sided submerged arc welded steel pipes, which is applicable to a steel pipe welding system. The steel pipe welding system includes a first infrared thermal imaging sensor, a second infrared thermal imaging sensor, a welding device, and a control terminal. The welding device moves along a first direction in which the steel pipe extends and welds one side of the steel pipe. The welding device, the first infrared thermal imaging sensor, and the second infrared thermal imaging sensor are sequentially collinearly arranged in a second direction opposite to the first direction. The method is applicable to the control terminal. Figure 1 , the method comprises the following steps: S101: Acquire a first heat source image based on a first infrared thermal imaging sensor, and acquire a second heat source image based on a second infrared sensor, wherein the first heat source image and the second heat source image are at the same position on the steel pipe.

[0027] As can be understood, the first and second infrared thermal imaging sensors capture heat source images at the same location on the steel pipe. Specifically, the two sensors first capture the first and second videos, respectively. The distance between the two sensors and the welding speed of the welding device are then determined. The first and second heat source images are then determined based on this data. Because the welding device and the two sensors are positioned in a specific orientation and the device is in motion, the time interval between the two images must be calculated to ensure they correspond to the same location on the pipe. This time interval is equal to the distance between the two sensors divided by the welding speed. The first heat source image is then taken by selecting the Mth frame from the first video and the Nth frame from the second video as the second heat source image. The difference in time between the two images is precisely equal to this time interval, ensuring they are in the same position on the pipe and providing accurate comparison for subsequent inspections.

[0028] Specifically, based on the first video obtained by the first infrared thermal imaging sensor and the second video obtained by the second infrared thermal imaging sensor, the interval distance between the first infrared thermal imaging sensor and the second infrared thermal imaging sensor, and the welding speed of the welding device when welding along the steel pipe can be obtained. Then, the first heat source image and the second heat source image can be determined based on the first video, the second video, the interval distance and the welding speed.

[0029] S102: Based on the first heat source image and the second heat source image, determine the target area from the second heat source image.

[0030] Based on the first heat source image and the second heat source image, a target area is determined from the second heat source image.

[0031] As an implementation method, a predicted heat source image can be determined based on the first heat source image, and then compared with the second heat source image. Specifically, the first heat source image is first divided into multiple sub-heat source images and the temperature data of each sub-image is obtained. Then, in combination with the steel pipe thermal diffusion model, the sub-heat source images are matched with the target volume area on the model. The second temperature data corresponding to each sub-heat source image is obtained through the model, and then the predicted heat source image is determined. The second heat source image is then divided into multiple sub-target images corresponding to the sub-heat source images, and each sub-target image is compared with the corresponding sub-heat source image. The area composed of those sub-target images with differences is the target area.

[0032] Exemplarily, in this embodiment, the predicted heat source image may be determined based on the first heat source image; The predicted heat source image is compared with the second heat source image, and based on the comparison result, the target area is determined from the second heat source image.

[0033] Specifically, the first heat source image can be divided into multiple sub-heat source images, and the temperature data corresponding to each sub-heat source image can be determined based on the first heat source image to obtain a steel pipe thermal diffusion model. Based on the position information of each sub-heat source image, each sub-heat source image can be corresponded to multiple target volume areas on the steel pipe thermal diffusion model. Based on the steel pipe thermal diffusion model, a target thermal diffusion model can be obtained. Then, based on the second temperature data corresponding to the multiple target volume areas in the target thermal diffusion model, the second temperature data corresponding to the multiple sub-heat source images can be determined. Finally, a predicted heat source image can be determined based on the multiple second temperature data.

[0034] It can be understood that the first heat source image is divided into multiple sub-heat source images and the corresponding temperature data is obtained. Combined with the steel pipe thermal diffusion model, the target thermal diffusion model and related second temperature data are obtained by corresponding the sub-heat source image position information with the target volume area on the model to determine the predicted heat source image.

[0035] Then, the second heat source image is segmented to form a plurality of sub-target images, wherein the plurality of sub-target images correspond to the plurality of sub-heat source images; Each sub-target image is compared with the sub-heat source image, and an area consisting of sub-target images different from the corresponding sub-heat source image among the multiple sub-target images is determined as the target area.

[0036] As can be understood, the segmentation of the sub-heat source image allows for more detailed temperature data acquisition. This, in conjunction with the target volume area in the steel pipe thermal diffusion model, allows the temperature change prediction to align with the heat transfer characteristics of the steel pipe during welding. The resulting predicted heat source image accurately reflects the expected heat source distribution at the same location at a later time, laying a precise foundation for comparison with the second heat source image and significantly reducing target area misjudgments due to prediction bias. Furthermore, systematic data processing and prediction using the thermal diffusion model replaces manual speculation on complex heat source changes, rapidly generating predicted heat source images and improving the overall efficiency of target area determination.

[0037] In some other embodiments, the method may further include determining the target area through the following steps.

[0038] S201: Divide the first heat source image into multiple sub-heat source images, and determine the temperature data corresponding to each sub-heat source image based on the first heat source image.

[0039] Specifically, the first heat source image is divided into multiple sub-heat source images according to preset rules (such as uniform grid division). This decomposes the original overall heat source information into smaller units of information, facilitating a more detailed analysis of the temperature distribution. Simultaneously, based on the temperature field information contained in the first heat source image, the temperature data corresponding to each sub-heat source image is extracted. This temperature data accurately reflects the thermal conditions of the sub-region at the time of capture, laying the data foundation for subsequent comparison with the sub-region of the second heat source image.

[0040] S202: The second heat source image is divided into multiple sub-target images, the multiple sub-target images correspond to the multiple sub-heat source images, and the target temperature data corresponding to each sub-target image is determined based on the second heat source image.

[0041] S203: Based on the correspondence between the sub-target images and the sub-heat source images, determine the temperature difference between each pair of corresponding sub-target images and sub-heat source images.

[0042] As can be understood, based on the correspondence between the sub-target images and the sub-heat source images established in S201 and S202, a difference calculation is performed on the temperature data of each pair of corresponding sub-target images and sub-heat source images to obtain a temperature difference value for each pair of sub-images. This temperature difference value intuitively reflects the temperature change of the sub-region at the same location at two different times and is an important basis for determining whether there is an abnormality in the region.

[0043] S204: Based on the position information of each sub-target image, determine the temperature difference between each pair of corresponding sub-target images and sub-heat source images, and the difference between the temperature differences of all adjacent pairs of sub-target images and sub-heat source images.

[0044] It can be understood that, based on the position information of each sub-target image in the second heat source image, the temperature difference corresponding to its adjacent sub-target images is determined. Then, the difference between the temperature difference of each pair of sub-images and the temperature difference of all adjacent pairs of sub-images is calculated. This difference can reflect the sudden change in temperature. If the temperature difference at a certain position shows a large sudden change compared with the adjacent positions, then this position may be at the boundary of the abnormal area.

[0045] S205: Obtain a preset difference value and compare the preset difference value with the temperature difference value. If the temperature difference between a pair of sub-target images and the sub-heat source image is greater than the temperature difference between two adjacent pairs of sub-target images and the sub-heat source image, the sub-target image is determined to be a target area boundary sub-image.

[0046] It can be understood that the calculated difference value is compared with the preset difference value. If the temperature difference between a pair of sub-target images and the sub-heat source image is greater than the temperature difference between the two adjacent pairs, it means that there is an abnormal mutation in the temperature change at the location of the sub-target image. Therefore, the sub-target image is determined as the boundary sub-image of the target area. These boundary sub-images are the key elements that constitute the boundary of the abnormal area.

[0047] S206: Based on the position information of the target area boundary sub-image in the second heat source image, fit the corresponding boundary of the target area and determine the target area.

[0048] As can be understood, based on the positional information of all target region boundary sub-images obtained in S205 within the second heat source image, a suitable fitting algorithm (such as curve fitting or polygon fitting) is employed to connect these boundary sub-images and fit the boundary corresponding to the target region. This boundary clearly delineates the region in the second heat source image where abnormal heat distribution exists, thereby determining the target region. This target region serves as the focus of subsequent analysis to determine whether there are quality defects in the steel pipe weld.

[0049] S103: Based on the regional characteristics of the target area, determine whether the steel pipe has quality defects during the welding process of the steel pipe.

[0050] Specifically, the regional characteristics of the target area can be obtained, and the regional characteristics include the area of ​​the target area, the temperature data in the target area, and the shape of the target area. Then, the preset area threshold, the preset temperature range, and the preset shape parameter range are obtained. Finally, the regional characteristics are compared with the preset area threshold, the preset temperature range, and the preset shape parameter range, and whether the steel pipe has quality defects is determined based on the comparison results.

[0051] Exemplarily, if the area of ​​the target region is less than or equal to a preset area threshold, the temperature data within the target region is compared with a preset target temperature range. If the temperature data within the target region exceeds the preset temperature range, it is preliminarily determined that the steel pipe may have quality defects. If the area of ​​the target area is less than or equal to the preset area threshold, and the temperature data in the target area is within the preset temperature range, the shape of the target area is compared with the preset shape parameter range. If the shape of the target area exceeds the preset shape parameter range, it is determined that the steel pipe has quality defects. If the shape of the target area is within the preset shape parameter range, it is determined that the steel pipe has no quality defects.

[0052] Understandably, when a pore defect appears within the material, the actual cooling rate of the corresponding area will be affected to some extent. Due to the presence of the defect, the cooling rate of the corresponding area may be slower than when the defect is not present. Therefore, the temperature change in the corresponding area can reveal whether there is a defect.

[0053] The large-diameter, double-sided, submerged arc welded steel pipe inspection method provided in this application uses two infrared thermal imaging sensors to capture heat source images of the same location on the pipe at different times. Image processing and analysis techniques are used to identify the target area, and weld quality is determined based on the characteristics of the target area. This enables real-time monitoring and quality inspection of the welding process. The method can capture and analyze images in real time during the welding process, allowing operators to promptly detect weld defects based on surface temperature changes, enabling them to adjust welding parameters and improve welding quality.

[0054] Based on the same inventive concept, the present application also proposes a large-diameter straight seam double-sided submerged arc welded steel pipe detection system, which includes a first infrared thermal imaging sensor, a second infrared thermal imaging sensor, a welding device, and a control terminal. The welding device moves along a first direction in which the steel pipe extends and welds one side of the steel pipe. The welding device, the first infrared thermal imaging sensor, and the second infrared thermal imaging sensor are collinearly arranged in a second direction opposite to the first direction. The system is configured as follows: A first heat source image is acquired based on a first infrared thermal imaging sensor, and a second heat source image is acquired based on a second infrared sensor, wherein the first heat source image and the second heat source image are located at the same position on the steel pipe; Based on the first heat source image and the second heat source image, determining a target area from the second heat source image; Based on the regional characteristics of the target area, it is determined whether quality defects occur in the steel pipe during the welding process.

[0055] Optionally, the system is configured to: A first heat source image is acquired based on a first infrared thermal imaging sensor, and a second heat source image is acquired based on a second infrared sensor, where the first heat source image and the second heat source image are located at the same position on the steel pipe, including: A first video acquired based on the first infrared thermal imaging sensor and a second video acquired based on the second infrared thermal imaging sensor; Obtaining the spacing between the first infrared thermal imaging sensor and the second infrared thermal imaging sensor, and the welding speed of the welding device when welding along the steel pipe; A first heat source image and a second heat source image are determined based on the first video, the second video, the spacing distance, and the welding speed.

[0056] Optionally, the system is configured to: Determining a first heat source image and a second heat source image based on the first video, the second video, the interval distance, and the welding speed includes: The M-th frame image is determined from the first video as the first heat source image, and the N-th frame image is determined from the second video as the second heat source image, wherein the shooting time corresponding to the M-th frame image and the shooting time corresponding to the N-th frame image are different from each other and are equal to the interval time divided by the welding speed.

[0057] Optionally, the system is configured to: Determining a target area from the second heat source image based on the first heat source image and the second heat source image includes: determining a predicted heat source image based on the first heat source image; The predicted heat source image is compared with the second heat source image, and based on the comparison result, the target area is determined from the second heat source image.

[0058] Optionally, the system is configured to: Determining a predicted image based on the first heat source image includes: Slicing the first heat source image to form a plurality of sub-heat source images, and determining temperature data corresponding to each sub-heat source image based on the first heat source image; Acquire a steel pipe thermal diffusion model, and based on position information of each sub-heat source image, correspond each sub-heat source image to a plurality of target volume areas on the steel pipe thermal diffusion model; Based on the steel pipe thermal diffusion model, the target thermal diffusion model is obtained; determining second temperature data corresponding to a plurality of sub-heat source images based on second temperature data corresponding to a plurality of target volume regions in a target thermal diffusion model; A predicted heat source image is determined based on the plurality of second temperature data.

[0059] Optionally, the system is configured to: Comparing the predicted heat source image with the second heat source image, and determining a target area from the second heat source image based on a comparison result, including: Segmenting the second heat source image to form a plurality of sub-target images, wherein the plurality of sub-target images correspond to the plurality of sub-heat source images; Each sub-target image is compared with the sub-heat source image, and an area consisting of sub-target images different from the corresponding sub-heat source image among the multiple sub-target images is determined as the target area.

[0060] Optionally, the system is configured to: Determining a target area from the second heat source image based on the first heat source image and the second heat source image includes: Slicing the first heat source image to form a plurality of sub-heat source images, and determining temperature data corresponding to each sub-heat source image based on the first heat source image; Segmenting the second heat source image to form a plurality of sub-target images, the plurality of sub-target images corresponding to the plurality of sub-heat source images, and determining target temperature data corresponding to each sub-target image based on the second heat source image; Based on the correspondence between the sub-target images and the sub-heat source images, determining the temperature difference between each pair of corresponding sub-target images and sub-heat source images; Based on the position information of each sub-target image, determining the temperature difference between each pair of corresponding sub-target images and sub-heat source images and the difference between the temperature differences of all adjacent pairs of sub-target images and sub-heat source images; Obtain a preset difference value, and compare the preset difference value with the temperature difference value. If the temperature difference between a pair of sub-target images and a sub-heat source image is greater than the temperature difference between two adjacent pairs of sub-target images and sub-heat source images, then determine that the sub-target image is a target area boundary sub-image; Based on the position information of the target area boundary sub-image in the second heat source image, the corresponding boundary of the target area is fitted, and the target area is determined.

[0061] Optionally, the system is configured to: Based on the regional characteristics of the target area, determine whether the steel pipe has quality defects during the welding process, including: Obtaining regional features of the target area, the regional features including the area of ​​the target area, the temperature data within the target area, and the shape of the target area; Obtaining a preset area threshold, a preset temperature range, and a preset shape parameter range; The regional features are compared with a preset area threshold, a preset temperature range, and a preset shape parameter range, and whether the steel pipe has quality defects is determined based on the comparison results.

[0062] The large-diameter, double-sided, straight-seam submerged arc welded steel pipe inspection system provided in this application uses two infrared thermal imaging sensors to capture heat source images of the same location on the steel pipe at different times. It then uses image processing and analysis techniques to identify the target area and, based on the characteristics of that area, to determine weld quality. This enables real-time monitoring and quality inspection of the welding process. The system can capture and analyze images in real time during the welding process, promptly identifying weld defects based on changes in surface temperature, allowing operators to adjust welding parameters and improve weld quality.

[0063] Based on the same inventive concept, an embodiment of the present application further provides an electronic device, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the large-diameter straight seam double-sided submerged arc welded steel pipe detection method of an embodiment of the present application.

[0064] In addition, to achieve the above-mentioned purpose, an embodiment of the present application also proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the large-diameter straight seam double-sided submerged arc welded steel pipe detection method of the embodiment of the present application.

[0065] The following is a detailed introduction to the various components of electronic equipment: The term "processor" refers to the control center of an electronic device and can be a single processor or a collective term for multiple processing elements. For example, the processor can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs) or one or more field programmable gate arrays (FPGAs).

[0066] Optionally, the processor can perform various functions of the electronic device by running or executing a software program stored in the memory, and calling data stored in the memory.

[0067] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0068] Alternatively, the memory may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. The memory may be integrated with the processor or exist independently and be coupled to the processor via an interface circuit of the electronic device, and this is not specifically limited in the embodiments of the present invention.

[0069] A transceiver is used to communicate with network devices or terminal devices.

[0070] Optionally, the transceiver may include a receiver and a transmitter, wherein the receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0071] Optionally, the transceiver may be integrated with the processor, or may exist independently and be coupled to the processor via an interface circuit of the router, which is not specifically limited in the embodiment of the present invention.

[0072] In addition, the technical effects of the electronic device can refer to the technical effects of the data transmission method in the above method embodiment, and will not be repeated here.

[0073] It should be understood that the processor in the embodiments of the present invention may be a central processing unit (CPU), but may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0074] It should also be understood that the memory in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0075] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0076] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0077] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0078] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0079] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

Claims

1. A large diameter straight seam double-sided submerged arc welded steel pipe detection method, characterized in that: The method is applicable to a steel pipe welding system, which includes a first infrared thermal imaging sensor, a second infrared thermal imaging sensor, a welding device, and a control terminal. The welding device moves along a first direction in which the steel pipe extends and welds one side surface of the steel pipe. The welding device, the first infrared thermal imaging sensor, and the second infrared thermal imaging sensor are sequentially collinearly arranged in a second direction opposite to the first direction. The method is applicable to the control terminal and includes: Acquire a first heat source image based on the first infrared thermal imaging sensor, and acquire a second heat source image based on the second infrared sensor, wherein the first heat source image and the second heat source image are located at the same position on the steel pipe; determining a target area from the second heat source image based on the first heat source image and the second heat source image; Based on the regional characteristics of the target area, it is determined whether the steel pipe has quality defects during the welding process of the steel pipe.

2. A large-diameter straight seam double-sided submerged arc welded steel pipe detection method according to claim 1, characterized in that: Acquiring a first heat source image based on the first infrared thermal imaging sensor and acquiring a second heat source image based on the second infrared sensor, where the first heat source image and the second heat source image are located at the same position on the steel pipe, includes: A first video acquired based on the first infrared thermal imaging sensor and a second video acquired based on the second infrared thermal imaging sensor; Obtaining a distance between the first infrared thermal imaging sensor and the second infrared thermal imaging sensor, and a welding speed of the welding device when welding along the steel pipe; The first heat source image and the second heat source image are determined based on the first video, the second video, the spacing distance, and the welding speed.

3. A large-diameter straight seam double-sided submerged arc welded steel pipe detection method according to claim 2, characterized in that: Determining the first heat source image and the second heat source image based on the first video, the second video, the interval distance, and the welding speed includes: Determine the Mth frame image from the first video as the first heat source image, and determine the Nth frame image from the second video as the second heat source image, wherein the shooting time corresponding to the Mth frame image and the shooting time corresponding to the Nth frame image are different from each other and are equal to the interval time divided by the welding speed.

4. A large diameter straight seam double-sided submerged arc welded steel pipe detection method according to claim 1, characterized in that: Determining a target area from the second heat source image based on the first heat source image and the second heat source image includes: determining a predicted heat source image based on the first heat source image; The predicted heat source image is compared with the second heat source image, and based on the comparison result, a target area is determined from the second heat source image.

5. A large-diameter straight seam double-sided submerged arc welded steel pipe detection method according to claim 4, characterized in that: Determining a predicted image based on the first heat source image includes: Slicing the first heat source image to form a plurality of sub-heat source images, and determining temperature data corresponding to each of the sub-heat source images based on the first heat source image; Acquire a steel pipe thermal diffusion model, and based on the position information of each of the sub-heat source images, correspond each of the sub-heat source images to a plurality of target volume areas on the steel pipe thermal diffusion model; Based on the steel pipe thermal diffusion model, obtaining a target thermal diffusion model; determining second temperature data corresponding to a plurality of the sub-heat source images based on second temperature data corresponding to a plurality of the target volume regions in the target thermal diffusion model; The predicted heat source image is determined based on a plurality of the second temperature data.

6. A large-diameter straight seam double-sided submerged arc welded steel pipe detection method according to claim 5, characterized in that: Comparing the predicted heat source image with the second heat source image, and determining a target area from the second heat source image according to a comparison result, includes: Segmenting the second heat source image to form a plurality of sub-target images, wherein the plurality of sub-target images correspond to the plurality of sub-heat source images; Each of the sub-target images is compared with the sub-heat source image, and an area composed of sub-target images that are different from the corresponding sub-heat source image among the multiple sub-target images is determined as the target area.

7. A large diameter straight seam double-sided submerged arc welded steel pipe detection method according to claim 1, characterized in that: Determining a target area from the second heat source image based on the first heat source image and the second heat source image includes: Slicing the first heat source image to form a plurality of sub-heat source images, and determining temperature data corresponding to each of the sub-heat source images based on the first heat source image; Segmenting the second heat source image to form a plurality of sub-target images, the plurality of sub-target images corresponding to the plurality of sub-heat source images, and determining target temperature data corresponding to each of the sub-target images based on the second heat source image; Determining the temperature difference between each pair of corresponding sub-target images and sub-heat source images based on the corresponding relationship between the sub-target images and the sub-heat source images; Based on the position information of each sub-target image, determining the temperature difference between each pair of corresponding sub-target images and the sub-heat source image and the difference between the temperature differences of all adjacent pairs of sub-target images and the sub-heat source images; Obtaining a preset difference value, and comparing the preset difference value with the temperature difference value; if the temperature difference value between a pair of the sub-target images and the sub-heat source image and the temperature difference values ​​between two adjacent pairs of the sub-target images and the sub-heat source images are greater than the preset difference value, determining that the sub-target image is a target area boundary sub-image; Based on the position information of the target area boundary sub-image in the second heat source image, the corresponding boundary of the target area is fitted, and the target area is determined.

8. The method for detecting large-diameter straight seam double-sided submerged arc welded steel pipe according to claim 1, characterized in that: Determining whether quality defects occur in the steel pipe during welding based on the regional characteristics of the target area includes: Acquiring regional features of the target region, wherein the regional features include the area of ​​the target region, temperature data within the target region, and the shape of the target region; Obtaining a preset area threshold, a preset temperature range, and a preset shape parameter range; The regional feature is compared with the preset area threshold, the preset temperature range, and the preset shape parameter range, and whether the steel pipe has quality defects is determined based on the comparison result.

9. A large diameter straight seam double-sided submerged arc welded steel pipe detection system, characterized in that: The system comprises a first infrared thermal imaging sensor, a second infrared thermal imaging sensor, a welding device, and a control terminal. The welding device moves along a first direction in which a steel pipe extends and welds one side surface of the steel pipe. The welding device, the first infrared thermal imaging sensor, and the second infrared thermal imaging sensor are collinearly arranged in a second direction opposite to the first direction. The system is configured as follows: Acquire a first heat source image based on the first infrared thermal imaging sensor, and acquire a second heat source image based on the second infrared sensor, wherein the first heat source image and the second heat source image are located at the same position on the steel pipe; determining a target area from the second heat source image based on the first heat source image and the second heat source image; Based on the regional characteristics of the target area, it is determined whether the steel pipe has quality defects during the welding process of the steel pipe.

10. An electronic device, characterized in that: include: at least one processor; and, a memory communicatively coupled to at least one of the processors; The memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors to enable the at least one processor to perform the method according to any one of claims 1 to 8.

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