# Efficient Metal Corrosion Area Detection Model Combining Convolution and Transformer
## 1 Introduction  
With the development of industrialization and modernization, metal materials have played an increasingly important role in industrial manufacturing and infrastructure construction. However, metal materials exposed to harsh environments over extended periods are highly prone to corrosion, leading to a decline in material quality. This degradation can result in severe safety incidents, financial losses, as well as environmental pollution, and health risks [1]. Studies over the past 50 years have shown that the cost of material corrosion is approximately equivalent to 3-4% of each country's Gross Domestic Product (GDP). Based on the global GDP of 3.4% in 2013, the global cost of corrosion is estimated to be around $2.5 trillion [2]. Furthermore, in industries such as petrochemicals, agriculture, and paper manufacturing, metal corrosion can lead to equipment damage and the leakage of harmful substances, significantly polluting the surrounding environment and posing serious health risks. Therefore, the ability to efficiently detect and locate metal corrosion areas at the early stages of rust formation, allowing for timely repair and maintenance, is of great engineering and economic significance. Currently, rust detection in engineering primarily relies on manual visual inspection, a method that suffers from low efficiency and high labor costs. As shown in Figure 1, inspection and treatment operations for artificially corroded areas inside large ship hulls are depicted. Figure 0(a) shows the interior of the large ship hull along with the industrial elevating work platform and operation platform, Figure 0(b) provides a close-up view of the manual operations, and Figure 0(c) offers a distant view of the manual operations. To improve detection efficiency, researchers have proposed detection methods based on physical and electrochemical sensors [3], which determine whether corrosion has occurred by monitoring the physical and chemical properties of metal surfaces. [PERSON] et al. designed a coating containing nanosensors to detect the presence of iron ions and changes in pH during the corrosion process of steel [4]. Additionally, corrosion defects in metals can be assessed through methods such as dye penetrant inspection, eddy current testing, thermal imaging, and ultrasonic testing. [PERSON] et al. employed high-frequency ultrasonic testing methods, including piezoelectric pulse-echo and laser ultrasonics, to determine the thickness of corrosion layers in nickel-based superalloys [5]. However, these traditional detection methods are limited by cost and application scenarios, making deployment challenging. With the rapid development of computer vision, corrosion detection methods based on digital image processing have gradually emerged [6]. [PERSON] et al. proposed a digital image algorithm for segmenting rust defects on painted steel surfaces, using a Gaussian mixture model for image segmentation, which improved the performance of rust area segmentation. However, the effectiveness of this method relies on the preset threshold [7]. [PERSON] et al. quantified two rust image features--roughness and color--to locate rust areas in the given image [8]. Compared to traditional manual inspection, digital image-based and pattern recognition methods offer advantages such as real-time performance and high efficiency, but they often require manual feature extraction and threshold setting, resulting in limited robustness. In recent years, deep learning-based detection technologies have been widely applied in various industrial scenarios, providing new solutions for detecting metal corrosion areas. [PERSON] et al. demonstrated that convolutional neural networks (CNNs) outperform texture and color feature analysis methods based on multi-layer perceptron networks in metal corrosion detection by comparing the performance of multiple methods [9]. [PERSON] et al.