Abstract: Traditional oil drill pipe flaw detection mainly relies on manual operation, requiring significant manpower and resources to complete the entire process, resulting in low efficiency and failing to meet the requirements for rapid flaw detection of large batches of pipes. Therefore, a high-efficiency and low-cost oil drill pipe flaw detection system was developed. Computer vision technology combined with specific hardware was used to achieve automatic detection and marking of cracks in oil drill pipes. This article describes the composition and function of the oil drill pipe flaw detection system, the flaw detection process, and the key technologies used. Keywords: Oil drill pipe; Crack detection; Visual C++ 6.0; Image processing Abstract: Traditional oil drill pipe crack detection mostly relies on manual operation. It requires a great deal of manpower and material resources to complete the entire working procedure with low efficiency, which cannot meet the requirement of fast crack detection for large quantities of oil drill pipes. Therefore, the author developed a high-efficiency and low-cost oil drill pipe crack detection system. This system, which utilizes Visual C++ 6.0 technology and considers material hardware, has achieved automatic crack detection and hit symbol detection on oil drill pipes. This paper narrates some important aspects such as system structure and functions, detection flow, and key technologies used in the system. Key words: Oil Drill Pipe; Crack detection; Visual C++ 6.0; Image Processing; 1 Introduction In the process of crude oil extraction, oil drill pipe is a major component connecting the surface rotation system, hoisting system, circulation system, drill collar, and drill bit, and its usage in oil fields is very large. Whether it can work normally and safely is one of the keys to the smooth progress of oilfield drilling projects. Drill pipes operate under harsh conditions, enduring various complex alternating loads underground, making them prone to fatigue cracks. Continuing to operate with cracked drill pipes risks breakage, and a drill string breakage leading to well burial can result in economic losses of millions, severely impacting the economic benefits of the drilling project. Therefore, timely crack detection of drill pipes is essential. Traditional oil drill pipe flaw detection relies mainly on manual interpretation, which is labor-intensive, inefficient, and cannot meet the requirements for rapid flaw detection of large quantities of drill pipes. Some oilfields have introduced automated pipe flaw detection production lines from abroad, but these are expensive and have excessively high production costs. This paper proposes a crack detection method using computer vision technology. Applying this method to oil drill pipe crack detection can significantly improve crack detection efficiency and reduce labor intensity and production costs. 2 System Composition and Functions First, the drill pipes to be inspected undergo pretreatment, such as cleaning, magnetization, and spraying with magnetic suspension, before being sent to the ultraviolet detection chamber for crack detection. The inspection system mainly consists of an image acquisition device, a stepping device, a marking device, a printing device, and a central control computer (hereinafter referred to as the central control computer). After the industrial CCD camera acquires images, they are transmitted to the central control computer. The central control computer performs crack detection on the images. If cracks are found, it outputs a marking signal to mark the drill pipe, saves the results, generates a crack information report, and then outputs a stepping signal to detect whether there are cracks in the next section of the drill pipe. Otherwise, it directly outputs a stepping signal. Based on the actual needs of the flaw detection process and customer requirements, the system has the following functions and features: ① Real-time dynamic display of monitoring images; ② Local or global image processing, including grayscale conversion, binarization, text editing, noise reduction, geometric magnification, reduction, rotation, movement, and region selection; ③ Identification and prompting of defect images, providing crack attributes (size, length, location, etc.); ④ Output of marking and stepping control signals; ⑤ Inspection report and image printing functions; ⑥ Automatic and manual operating modes. The system software adopts a single-document dual-view structure, taking into account the user's requirement for real-time dynamic display of monitoring images. The left view is used to display monitoring images in real time, place some operation buttons, output detection results (displayed in red for emphasis), and generate crack attribute reports; the right view serves as the image display window, and as the output window for image processing results when the user selects manual detection; a collapse button is set in the center of the blue dividing bar between the two views, which the user can click to hide and show the left view to obtain more image processing space. The running results show that this structure can well meet the user's needs and also makes the interface beautiful and elegant. The system running screen is shown in Figure 1. [align=center] Figure 1. System running diagram[/align] In order to make the flaw detection process accurate and fast, the detection software performs normalization processing on the obtained drill pipe images (directly acquired by CCD camera, opened by mouse drag, opened by recently opened file list, etc.), that is, the image format to be processed is unified as follows: ① Normalize the color palette information. When converting an image to grayscale, the image palette information is unified in terms of grayscale levels. The palette array ranges from 0 to 255 from the first data point to the last, for a total of 256 levels. Simultaneously, relevant statistical information in the bitmap header is modified, such as biClrUsed=256 and biBitCount=8. ② Normalize the image bitmap data area. Based on the normalized palette, the bitmap data is unified, standardizing the color index values ​​for monochrome bitmaps, 16-color bitmaps, and 256-color bitmaps without a normalized palette. For example, monochrome bitmaps use index values ​​of 0 and 255 to fill the data area. The advantage of normalization is that one byte of bitmap data can represent exactly one pixel, simplifying the complexity of program processing and improving crack detection speed. The program is finally implemented using Visual C++ 6.0. During software operation, the 24-bit color bitmap is first converted to grayscale and then binarized. The binarized image is then filtered for noise reduction, and finally, crack detection is performed. The image obtained during manual inspection in the laboratory is shown below: Figure 2. Detection sequence image 3. Flaw detection process The pre-processed oil drill pipe is fixed on a base equipped with a stepping device and sent to the fluorescence laboratory. The central control computer sends a stepping command, and the drill pipe steps into the CCD camera's field of view. After stepping stops, the industrial camera acquires the image and sends it to the central control computer for processing. The oil drill pipe flaw detection software inside the central control computer automatically converts the image to grayscale and binarizes it, then performs median filtering and mathematical morphological opening and closing operations for noise reduction. This completes the pre-processing operation of the oil drill pipe image. Figure 3. Flaw Detection Flowchart. The flaw detection software performs horizontal and vertical scanning and projection calculations on the pre-processed binary image of the drill pipe. If no suspicious target area is found, a step command is sent to perform crack detection on the next section of the drill pipe; otherwise, the target area is saved and sent to the crack detection module. The crack detection module uses a circularity-based crack detection algorithm to further determine the suspicious target area. If no crack exists, a step command is sent; otherwise, an alarm signal is issued, crack attribute information is output, a result report is generated, and a marking signal is output to control the marking device to mark the crack. Finally, a step command is sent to perform crack detection on the remaining drill pipes until one drill pipe has been inspected. In the entire process, image preprocessing is a crucial step; the quality of preprocessing directly affects the final detection result. The current system can only detect cracks in one video signal. Since the drill pipe is cylindrical, in a real-world system, at least three CCD cameras are needed to cover the entire drill pipe circumference to achieve simultaneous and complete inspection. Modifications to the flaw detection software program are required for multi-channel signal processing, employing a multi-threaded mechanism to achieve parallel detection of multiple drill pipe cracks. The flaw detection flowchart is shown in Figure 3. 4 Key Technologies 4.1 Computer Vision Technology Humans primarily perceive external information through vision; over 80% of human perception is obtained visually. Since the advent of signal processing theory and computers, people have attempted to use cameras to obtain environmental images and convert them into digital signals, using computers to realize the entire process of visual information processing. This has led to the formation of a new discipline—computer vision. Computer vision is a highly interdisciplinary field, involving computer science, psychology, physiology, physics, signal processing, and mathematics. In recent years, with the development of various disciplines and computer technology, computer vision has been widely applied in areas such as mobile robot visual navigation, medical auxiliary diagnosis, industrial robot eyes, object 3D shape analysis and recognition, intelligent human-machine interfaces, and intelligent monitoring. 4.2 Targeted crack image preprocessing methods Image preprocessing includes grayscale conversion, binarization and noise elimination. During binarization, since the crack area occupies a small proportion in the image, the result of binarization using the average threshold method needs to be thresholded. The laboratory test data suggests that the average threshold should be reduced by 25 orders of magnitude (grayscale is 256 levels). Macroscopic defects of drill pipes can be divided into two categories. The first category: longitudinal tearing type linear cracks on the outer surface. These cracks are visible to the naked eye on the outer surface of the pipe body, with a width of 0.5-2 mm, a depth of more than 0.5 mm, and a length of less than 50 mm [5], and are close to the "thickening transition zone". The second category: internal cracks, which cannot be detected by the system, so they will not be discussed further. For the first type of longitudinal tearing external surface cracks, longitudinal length and thinness are common attributes of this type of crack. Therefore, in the noise reduction stage, a targeted strip noise reduction template is used to perform opening and closing operations in binary morphology, which can effectively remove transverse interference, block noise, and discrete point noise, greatly improving the accuracy of crack detection. 4.3 Drill pipe image crack detection algorithm After preprocessing, the image is scanned and projected to obtain the target areas that may be cracks. The crack detection module uses these target areas as input to perform crack identification, mainly removing pseudo-cracks formed by interference factors such as mud, cement, and corrosion points that have not been cleaned, to obtain the real cracks, and finally outputs crack attribute information. The algorithm for determining cracks is the currently popular circularity judgment algorithm. This algorithm calculates c = 4 * PI * s / (l * l) based on the perimeter l and area s of the suspected target area, where PI = 3.14159265. Then, a threshold judgment is applied to the result to determine whether it is a real crack. The threshold value is determined experimentally, and 0.3 is used in the experiment. If it is greater than this value, it is not a crack; otherwise, it is judged as a crack. This can effectively remove the circular and square interference in the binary image and obtain the true slender crack information. 5 Conclusion This paper designs a flaw detection system for oil drill pipe. In the system design, computer vision technology, Visual C++ 6.0 hardware access control technology, image smoothing and noise reduction preprocessing technology, and crack detection technology are comprehensively applied to make the system reliable and practical. In addition, the system has a good human-computer interface and is simple to use. Experimental results show that the flaw detection method implemented by this system is feasible. The innovation of this paper is: ① Applying computer vision technology to crack detection of oil drill pipe. ②A practical flaw detection system was designed and implemented for the specific situation of crack detection of oil drill pipe. References: [1] Zhang Qingshe. Fluorescent magnetic particle flaw detection and defect analysis of threads of oil special drill tools. Nondestructive Testing, 2004.5:256-257 [2] Ren Yong, Li Jing, Zhang Xi. Automated production line for nondestructive testing of oil special pipes. Petroleum Instruments, 2003.10:24-26 [3] Li Xiaopeng, Ma Xiaohuang. Delphi implementation of remote monitoring system based on military information network. Microcomputer Information, 2006.8:86-87 [4] Sun Jinli, Chen Xinbo, Yuan Yingmin, et al. Detection of fatigue cracks in oilfield drill tool joints with eddy current shielded probe. Nondestructive Testing, 2002.12:542-543 [5] Zhou Mengxiong, Peng Guoliang, Qiu Ning. Crack analysis of oil drill pipe. Steel Pipe, 2004.10:11-13. Author Biographies: An Hongzheng, male, born February 1983, Master's student, research direction: Computer Application Technology. Yin Xiaochuan, male, born January 1961, Professor, research direction: Computer Application Technology. Wang Bei, female, born May 1979, Master's student, research direction: Computer Application Technology.