## 1 Introduction
There are also a small number of studies that are not based on hyperbolic features, such as identifying and locating debonding, water damage, looseness, and settlement. Residual networks have been widely utilized for the identification of hidden distresses due to their exceptional performance. For instance, ResNet-50 was employed for subgrade distress classification and yielded favorable results [19]. Meanwhile, the combination of ResNet-50 and YOLOv2 in detection frameworks showed promising performance in detecting water damage on pavement [20]. Additionally, the use of ResNet-50, EfficientNet, and VIT in distress recognition based on GPR images demonstrated reliable performance [21]. VGG16 and ResNet-50 were applied for distress classification based on 3D-GPR with satisfactory results [22]. In addition to residual networks, other network architectures are also widely used for hidden distresses identification. For instance, a backpropagation neural network method was proposed to recognize loose damage in pavement based on GPR A-scans [23], which can reduce the reliance on empirical judgment in GPR image interpretation. Meanwhile, as the advantages of CNNs in classification and regression tasks, the classification and the regression network architecture were designed separately to classify moisture quantitatively and predict water content based on GPR A-scans [24]. Additionally, DenseNet was used as a backbone of a novel B-scan image generator, and detected distresses based on the simulation data mixed with real B-scan images [25]. And a comparison among Xception, Inception, EfficientNet, and MobileNet using GPR images revealed that MobileNet exhibited the best performance [26], while an analysis indicated that SqueezeNet performs well too [27]. Based on GPR data, two network-in-network were respectively used to recognize and locate water-damage pits and uneven settlements [28]. And an improved two-way object detection model with the backbone of Res2 Net for targets of different scales was proposed to detect settlement and loosening in pavement [29]. Moreover, YOLOv3 with the backbones of DarkNet-53 and U-Net model were combined to detect and segment debonding in pavement, which made further advances in automatically detecting and locating the distresses in pavement [30]. However, there is limited research on the identification of interlayer distresses. In recent years, we have carried out distress detection on several highways in Jiangsu Province and found that most of the distresses are interlayer distresses [31,32]. Through the comprehensive analysis of interlayer distresses, the initial manifestation is the lack of adhesion between highway layers, leading to water infiltration and subsequent destruction of the pavement structure. Ultimately, when interlayer water seepage occurs, it is not repaired in time, and the action of load results in interlayer loosening. Therefore, the interlayer distresses may be further categorized as interlayer debonding, interlayer water seepage, and interlayer loosening (refer to Section 2.1 for specific descriptions). Nevertheless, achieving precise automatic identification of these interlayer distresses remains a formidable challenge. For the time being, deep CNN has emerged as the leading approach in image analysis, significantly advancing various classification tasks with performance that often surpasses human accuracy. The deep CNN has thus become the mainstream method for distress classification in GPR data [33], such as ResNet, DenseNet, EfficientNet, SqueezeNet, MobileNet, and VGG. However, among models of different complexities, which models are suitable for interlayer distress data needs further verification. Among these deep learning architectures, ResNet [34] performs well in top-1 accuracy [35], especially as the residual structure makes it easier to build shallower or deeper networks in the construction of different complexity.