# Efficient Metal Corrosion Area Detection Model Combining Convolution and Transformer
## 4 Engineering Application  
As shown in Figure 15, under laboratory conditions, we conducted tests for detecting corrosion areas on the inner surfaces of a simulated ship cabin using an edge computing platform based on the NVIDIA Jetson Xavier NX, an RGB camera, and a PC, achieving FPS values exceeding 30. The experimental results demonstrated that the MCD-Net model effectively identified smaller corrosion areas and connected them to form larger regions requiring de-rusting. The right side of the figure shows the model's corrosion detection results, successfully locating the sections that need treatment with good performance.  
As shown in Figure 16, on actual ship steel plates, we successfully completed corrosion detection, de-rusting operations, and post-de-rusting secondary detection. Initially, we performed a preliminary detection of corrosion areas on the untreated steel plates, and the MCD-Net model accurately identified the areas requiring treatment. Afterward, we used high-pressure water jetting to clean some of the corroded areas. We then conducted a secondary corrosion detection, and the results showed that the cleaned areas were identified as corrosion-free, verifying the effectiveness of the de-rusting operation and the accuracy of corrosion area detection. This provided effective technical support for ship surface maintenance.  
The next step in this study is to utilize the MCD-Net metal corrosion detection network to achieve automated de-rusting within the ship cabin. As shown in Figure 17, the entire system includes an Intel mini industrial control computer, combined with an RGB camera and an Intel RealSense D435i depth camera module, an air knife (to prevent water mist generated during de-rusting from obstructing the camera), a collaborative robot, a de-rusting nozzle, a nozzle fixture, and a movable platform.  
As shown in Figure 18, in the ship steel plate de-rusting scenario, the research team used the depth camera to perform three-dimensional (3D) object detection, accurately extracting the key point coordinates of the target workpiece. These key points were used to drive a six-degree-of-freedom robotic arm, which carried an end-effector de-rusting tool to approach and act on the workpiece surface at the appropriate angle, ensuring de-rusting efficiency. During the de-rusting operation, the equipment supports both high-pressure water jetting and ice particle de-rusting, enabling flexible adaptation to different de-rusting needs. The entire system is integrated into a mobile elevating work platform, offering flexible deployment capabilities to efficiently complete ship surface de-rusting tasks.  
Figure 15: Test experiment scenario in laboratory conditions: (**a**) Experimental platform; (**b**) simulated corroded area on the interior surface of the ship hull; (**c**) detection result.  
Figure 16: Corrosion detection, de-rusting operations, and post-de-rusting secondary detection on actual ship steel plates: (**a**) Actual ship steel plates; (**b**) initial detection of corroded areas on the ship steel plate surface; (**c**) high-pressure water jet cleaning; (**d**) partially de-rusted ship steel plates; (**e**) secondary detection of corroded areas after de-rusting.  
Figure 17: Automated de-rusting system within the ship cabin: (**a**) Simulated diagram of the system; (**b**) physical diagram of the system.