## 1 Introduction
[15] introduced a method for identifying corrosion areas in offshore steel structures, first constructing a deep learning-based image classification model to classify different types of corrosion, and then using digital image processing techniques to extract the corrosion area information. Object detection and semantic segmentation algorithms, which are downstream tasks of classification algorithms, are more commonly used in practical corrosion detection tasks. Object detection can pinpoint the coordinates of corrosion locations. [PERSON] et al. [16] proposed a YOLOv5-based corrosion detection method for detecting corrosion on coastal steel structures. [PERSON] et al. [17] introduced a two-stage corrosion detection method that combines a generative adversarial network (GAN) with an improved YOLOv5 network, enabling rapid classification of bridge bolts. [PERSON] et al. [18] developed a structure visual inspection method based on Faster R-CNN and constructed a defect dataset containing five categories: concrete cracks, steel corrosion, bolt corrosion, and steel delamination, to train the proposed method. [PERSON] et al. [19] proposed an automatic detection method for carbon steel corrosion severity based on YOLOv7, incorporating the convolutional block attention module (CBAM) module to improve network performance and using data augmentation and background fusion techniques to enhance the diversity of the carbon steel corrosion dataset. [PERSON] et al. [20] also introduced a YOLOv7-based method for steel bridge corrosion detection, investigating the impact of dataset quality on model detection accuracy. They built a high-quality dataset and achieved corrosion detection on bridges using drones. Semantic segmentation can pinpoint the pixel-level location of corrosion. [PERSON] et al. [21] proposed a fusion attention u-net (FAU) network based on U-Net for detecting corrosion inside dim steel box girders, and studied the effects of shooting distance, focal length, and angle, achieving pixel-level corrosion detection. [PERSON] et al. [22] developed a lightweight U-Net model to segment corrosion areas and enhance U-Net with a residual attention module for corrosion grade classification. In another study by the same authors [23], they found that various semantic segmentation networks (e.g., FCN, U-Net, and Mask R-CNN) struggled to meet the requirements of corrosion detection tasks, so color segmentation results were incorporated to further improve corrosion segmentation performance. [PERSON] et al. [24] proposed a corrosion detection framework for container surfaces, which included two corrosion segmentation modules (improved based on Mask R-CNN) and a corrosion defect characterization module (using optical flow methods to stitch images and calculate the overall corrosion rate of the container surface). [PERSON] et al. [25] introduced a corrosion detection method called CorrDetector, which uses drone imagery to detect corrosion on transmission towers. The algorithm first employs a Mask R-CNN-based semantic segmentation algorithm to segment the contours of the transmission towers, followed by a CNN-based classification model for corrosion detection. [PERSON] et al. [26] proposed a deep learning-based corrosion detection method, achieving pixel-level detection of corrosion on steel structures and introducing three Bayesian variables to describe the confidence of each pixel. [PERSON] et al. [27] proposed the AECIF-Net for bridge corrosion detection, which can simultaneously detect structural information and segment corrosion on structures, showing better results compared to current state-of-the-art methods. [PERSON] et al. [28] introduced material and damage network (MaDnet), a deep neural network capable of recognizing multiple material types (concrete, steel, and asphalt) and various structural damages (cracks, exposed rebar, spalling, and corrosion). MaDnet performed well on the developed dataset for segmentation tasks.