## 1 Introduction  
Tunnels can improve the operational efficiency of transportation systems. However, most tunnels operate in harsh environments, which lead to the inevitable cracking of the tunnel lining surfaces [1]. As cracks in tunnel linings threaten structural integrity and pose safety risks, timely crack inspection is essential for maintaining the structural safety of tunnel linings [2]. However, conventional crack detection methods rely on manual inspection, which is not only time-consuming and laborious, but its accuracy is also limited by the subjective experience of the inspector [3]. In addition, the increasing number of tunnels in operation requires more automated and reliable inspection methodologies. Image recognition methodology has recently offered a promising alternative for structural crack monitoring, as it can potentially replace manual inspection and automate the realization of the crack detection process [4, 5]. Image recognition methodology for crack detection can be categorized into model-driven and data-driven technologies [6, 7]. A model-driven method is represented by traditional digital image methods, which design algorithms around a specific recognition target object to extract features and separate the target object from the background in the digital image. These methods typically involve smoothing the image with mean, median or Gaussian filters, and algorithms based on Otsu thresholding segmentation and edge detection enhancements are often used for crack detection [8, 9, 10]. However,the above methods may suffer from low accuracy and generalization in tunnel scenes with interference backgrounds and variable illumination owing to the limitations of the designer's experiences. Data-driven methods based on deep learning, especially convolutional neural networks (CNNs) [11; 12; 13; 14], have outperformed model-driven methods in crack detection, as demonstrated by numerous studies [5; 15; 16]. [PERSON] et al. used CNNs to classify road crack image patches captured by smartphones, and the results revealed that CNNs' detection performance was better than the traditional manual feature extraction method [17]. [PERSON] et al. applied transfer learning with VGGNet to various structural damages, revealing the potential of deep learning in structural damage detection [18]. [PERSON] et al. achieved a classification accuracy of up to 98.26% on a small sample dataset using transfer learning optimized AlexNet [19]. However, the above models require high computational power in practical applications, which is challenging to apply on mobile devices with limited computational resources. Recently, researchers have made progress in developing lightweight convolutional neural networks (LCNNs); various LCNNs have achieved high accuracy and low computational cost in image classification. For example, Google proposed MobileNetV1 [20] in 2017, incorporating depthwise separable convolution for feature extraction to reduce computational resources. A subsequent study on MobileNetV2 [21] introduced an inverted residual block, and further enhancements were made in MobileNetV3 [22] by utilizing the hard-swish activation function and the squeeze-excitation (SE) attention mechanism. [PERSON] et al. proposed ShuffleNetV1 [23], which employed a channel shuffle for additional computational efficiency. Then, they proposed a lightweight network design guideline, which was utilized for developing ShuffleNetV2 [24]. EfficientNet [25], another impressive design by [PERSON] et al., investigated the effects of image input resolution, network depth and network width rationalization on the classification results. Thanks to the development of LCNNs, they have emerged as promising alternatives for crack detection because of their potential use in mobile devices [26]. [PERSON] et al. proposed MobileCrack, an LCNN model for pavement classification with only a quarter of the parameters of MobileNet [27]. [PERSON] et al.