# Hybrid-Supervised-Learning-Based Automatic Image Segmentation for Water Leakage in Subway Tunnels
###### Abstract
[22] proposed a defect segmentation system based on weakly supervised learning, which improves the accuracy of classification and segmentation, but the efficiency is not high, and the problem of time-consuming data labeling has not been solved. [PERSON] et al. [23] used Mask R-CNN for instance segmentation of water leakage in tunnel linings. The time-consuming problem was solved without overfitting, but there was a problem of low segmentation accuracy. [PERSON] et al. [24] proposed a new network framework as the backbone of the weakly supervised network and added the K-means clustering algorithm to improve the segmentation accuracy. However, due to the limitations of K-means, the image segmentation effect is not ideal in complex environments. Automatic detection has become the mainstream trend of current research, which can ensure detection accuracy while ensuring efficiency. [PERSON] et al. [25] proposed a new method for automated pixel-level crack detection on 3D asphalt pavement and called it CrackNet-R. This method can achieve high precision in crack detection. However, it can easily create technical isolation for users in practical applications. [PERSON] et al. [26], [PERSON] et al. [27], and [PERSON] et al. [28] applied a fully convolutional network (FCN) to perform automatic pixel-level crack detection. This method has a good effect on large-pixel crack detection but has certain limitations for small cracks, and the effect is not ideal. [PERSON] et al. [29] proposed an encoder-decoder network-based method for the automatic detection of road cracks at the pixel level. Transfer learning is performed using ResNet152, which achieves the best performance among several convolutional neural networks, but the experimental results do not achieve the expected accuracy. [PERSON] et al. [30] propose the use of U-Net for automatic crack detection. U-Net has better robustness than [PERSON]'s CNN method and is more suitable for crack detection. However, due to the complex environment and interference, it will generate redundant identification. [PERSON] et al. [13] proposed an improved automatic crack detection framework based on DeepLabV3+ for detection and obtained a speed of 23 frames per second, but the robustness is not strong, only for small data samples. Therefore, automatic object detection is still a challenge for pixel segmentation. At present, there are still problems such as complex algorithms, long time consumption, and low detection accuracy in automatic detection. This paper proposes a lightweight hybrid supervised learning (HSL) approach that combines efficient weakly supervised learning with high-precision, fully supervised learning. Among them, weakly supervised learning is used for the automatic generation of water leakage pixel-level labels, while fully supervised learning is used for water leakage semantic segmentation. In weakly supervised learning, the Water Leakage-CAM (WL-CAM) method framework is proposed. Through a multi-level fusion strategy, high-precision pixel-level labels are obtained. First, an adaptive pixel segmentation clustering (APS) algorithm is used to generate image-level labels with less background information. Second, the WRes2 Net deep learning network is used for training, and the class activation map (CAM) generated by training is used to generate label files and image-level labels for pixel value overlap and fusion, thereby reducing data labeling time and labor costs. In fully supervised learning, this paper improves the DeepLabV3+ network, adding multiple learning rate decay methods,optimizers, and attention mechanisms. It can accurately segment water leakage in various complex environments, and the segmentation accuracy is greatly improved.