## 1 Introduction  
The effective operation of industrial systems relies heavily on the accurate collection of equipment information during maintenance. Industrial nameplates are a vital form of identification, displaying critical data and parameters. The use of optical character recognition (OCR) algorithms to extract information from nameplates has become a key technology in the field of intelligent industrial production, providing valuable support to operators. However, aging, corrosion, rust, and other defects can render nameplates difficult to read using OCR algorithms, leading to errors in equipment identification. Therefore, the development of effective methods for safely, quickly, and efficiently capturing equipment information has become a prominent area of research in the fields of computer vision and industrial scenarios. The ability to accurately extract information from damaged nameplates has significant scientific and practical value, enabling efficient maintenance and ensuring the safe and reliable operation of industrial systems. Artificial intelligence development is currently at the stage referred to as weak artificial intelligence [1, 2]. Object detection, as a crucial component of OCR, leverages predefined data sets to locate objects or their key features for recognition. There are established examples of computer vision systems in complex conditions. For example, the detection of traffic signs in complex scenarios is a significant challenge in the field of autonomous driving, as presented by [PERSON] et al. [3]. The authors of the paper suggests a new information acquisition method that could open up new research possibilities for computer vision techniques in industry. The photographs of equipment nameplates may present limitations such as varied shooting conditions and a high degree of irrelevant information. To accurately identify the nameplate location, we created the Equipment Nameplate Dataset (MEND) and built a machine learning algorithm that is based on the use of Probabilistic Hough Transform. Elimination of external regions of the image is performed by perspective modification. Comparison experiments using DBNet [4] were conducted on a subset of nameplate images from the MEND set, yielding effective detection results with high computational efficiency. Environmental conditions and image information quality can greatly impact the accuracy of text detection in industrial nameplates [5]. The accuracy of text recognition for industrial nameplates depends heavily on image quality and the conditions in which images are taken. Any minor alterations to the image, like fuzziness or static, or any environmental changes, such as brightness, can significantly hamper the precision of the text recognition procedure [6]. Text detection in industrial environment faces many difficulties, as shown in Figure 1.  
There are a range of strategies that can be implemented to mitigate the negative consequences of text detection. If one wants to prevent any undesirable results, the visual realism of the image can be modified. By taking a close look at the image and making the necessary changes to brightness, hue, or contrast, one can achieve the desired result [7]. Taking this approach will guarantee that the image appears as natural as it can, and any undesirable results are avoided. Filtering techniques can be employed to heighten the quality of digital images, thus diminishing the amount of image noise [7; 8; 9]. Various strategies like blurring, smoothing, and sharpening are utilized in these approaches to decrease the quantity of unnecessary noise in the picture. Methods of sifting can be utilized to maintain the vital aspects of an image while discarding undesirable components, such as static. However, filtering techniques can only process images with a certain texture pattern, and once the noise has changed due to the industrial production environment, the algorithm cannot adjust. Frequency domain analysis and threshold segmentation are effective strategies for emphasizing key elements in images [5; 9].