# Fatigue Crack Detection Based on Semantic Segmentation Using DeepLabV3+ for Steel Girder Bridges  
[PERSON]  
1 College of Transportation Engineering, Nanjing Technology University, Nanjing 211899, China; 1 [EMAIL_ADDRESS]  
[PERSON]  
3 School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China  
CNNs have gained popularity due to their superior performance in image-based applications. CNNs can automatically extract features from raw image data, making them highly effective for crack detection in bridge structures. By processing images captured from inspections, CNNs can identify and classify cracks with high precision. [PERSON] et al. [3] developed a CNN model for crack detection in steel bridges using images taken by drones. The model achieved an accuracy of over 95% in identifying fatigue cracks. Furthermore, [PERSON] and [PERSON] [4] designed a multi-scale CNN to detect cracks of varying sizes and orientations, demonstrating robust performances under different lighting and environmental conditions.  
Semantic segmentation networks, such as DeepLabv3+ [5] and U-Net, are advanced deep learning models that provide pixel-level classification, allowing for the precise localization and segmentation of cracks in images. These networks are particularly useful for identifying small and complex crack patterns that might be missed by traditional methods. [PERSON] et al. [6] introduced DeepLabv3+, which utilizes atrous spatial pyramid pooling to capture multi-scale contextual information. This model has been applied to crack detection, offering high accuracy in segmenting cracks from background noise. Similarly, [PERSON] et al. [7] proposed the U-Net architecture, which has been widely adopted for medical image segmentation and recently adapted for structural health monitoring tasks.  
Transfer learning involves pre-training a model on a large dataset and fine-tuning it on a smaller, specific dataset. This approach is beneficial when the labeled data for crack detection are limited, as it leverages the knowledge gained from related tasks. [PERSON] et al. [8] applied transfer learning by fine-tuning a pre-trained ResNet model on a dataset of bridge crack images. The transfer learning approach significantly improved the detection accuracy, especially in scenarios with limited training data.  
The integration of AI techniques into fatigue crack detection for steel girder bridges has significantly advanced the field of structural health monitoring. However, developing more robust models that can handle diverse environmental disturbance and provide more accurate prediction results requires further investigation. Aiming to achieve this objective, this paper presents a semantic segmentation model for fatigue crack detection based on DeepLabv3+, ResNet50, and active learning.  
This paper is organized as follows. Section 2 provides a brief introduction to DeepLabv3+. The proposed fatigue crack detection model is presented in Section 3. In Section 4, relevant models are compared in terms of validation accuracy and mean intersection over union (MIoU). To further improve the model performance and robustness, the pixel category threshold optimization is discussed in Section 5. Section 6 examines and presents the compound model by comparing the predicted fatigue cracks with the ground truth. Finally, the main contents are summarized in Section 7.