## 1 Introduction
Therefore, in recent years, CNN has also been introduced into ultrasonic testing signal classification. Research by [PERSON] et al. [24] showed that CNN successfully classified ultrasonic weldment flaw A-scan signals while maintaining good performance in the presence of noise. [PERSON] et al. [25] proposed a CNN architecture to detect defects in ultrasonic signals. They first decomposed the A-scan signal using discrete wavelet transform with four-level decomposition and then reorganized the wavelet coefficients as a two-dimensional input for the model. In addition, [PERSON] et al. [26] applied a pre-trained neural network to detect flaws in concrete from images of the ultrasonic B-scan. [PERSON] et al. [27] proposed a CNN structure that integrated a support vector machine to identify cracking-related A-scan signals obtained from pipeline girth welds. [PERSON] et al. [28] conducted a study to compare the effectiveness of machine learning techniques, specifically deep learning, for automating the assessment of ultrasonic A-scan signals from butt-fused joints in PE pipes. Their findings suggest that CNN was the most performant machine learning approach. [PERSON] et al. [29] proposed an intelligent recognition method based on wavelet packet transform (WPT) and CNN for concrete ultrasonic detection, which resulted in outstanding recognition performance. [PERSON] et al. [30] obtained a classification accuracy rate of up to 0.982 using CNN and ultrasonic A-scan to evaluate circumferential welds composed of austenitic and martensitic stainless steel with internal slots. These studies illustrate the capability of deep learning, specifically CNN, for identifying different types of ultrasonic defect signals. As the defect signal in the blind zone is not easy to distinguish in the time domain, converting the signal to the time-frequency domain can provide more abundant information. On this basis, CNN can potentially be applied to the classification of near-surface defects by learning the key information of signals in the time-frequency domain. This paper proposes a new approach to detect and locate near-surface defects by leveraging SST while also designing a lightweight CNN model to identify the types of defects. Through the integration of these two techniques, our approach accomplishes near-surface defect detection, localization, and identification with high accuracy. The proposed model employs DenseNet [31] as the backbone to reuse model features, employs depthwise separable convolution (DSC) [32] instead of ordinary convolution to reduce the model parameters, and incorporates the convolutional block attention module (CBAM) [33] to highlight key information with high weights in the final decision. The subsequent sections are structured as follows: Section 2 presents the theory of SST. Section 3 outlines the equipment used and the preparation of the dataset. Section 4 provides details on the near-surface defect detection method based on SST and the architecture of the defect identification model, which includes DenseNet structure, DSC, and CBAM. Section 5 illustrates the results of the experiments on the proposed model. Finally, Section 6 concludes the paper and presents a summary.