## 1 Introduction  
Polyethylene (PE) is widely utilized in oil and gas transmission pipelines due to its high-impact strength, heat resistance, corrosion resistance, ease of installation, and excellent electrical properties. Hot melt welding is a commonly used method for welding PE pipes. However, due to PE's low sound speed and significant sound energy attenuation, it is necessary to inspect the weld quality using a low-frequency ultrasonic transducer after hot melt welding [1]. The transducer's blind zone widens with decreasing frequency, making it difficult to identify near surface defects that are buried within this zone. This issue also affects inspectors using water immersion ultrasonic testing [2], where near-surface defect signals can overlap with front-wall echoes, causing inaccurate evaluations of workpiece quality. Scholars have attempted to address the issue of near-surface defect testing from two main perspectives. Firstly, some researchers have attempted to reduce the influence of the blind zone by enhancing the performance of the transducer. [PERSON] et al. [3] presented a new coding mode based on Golay complementary pairs, which helped to reduce the range of the blind zone. Another study by [PERSON] et al. [4] utilized an opposite phase superposition method to reduce the pulse signal duration, and [PERSON] et al. [5] proposeda novel ultrasonic transducer that utilized a rectangular membrane with a large aspect ratio and multiple resonant modes to obtain a wide-band signal. However, despite these hardware modifications, the elimination of the blind zone remains challenging. At the same time, many researchers have turned to signal processing methods to analyze the features of the defect echoes, including Hilbert transform [6], energy cepstrum [7], split spectrum [8], cross-correlation functions [6], wavelet packet decomposition [9], deconvolution [10; 11; 12], and pulse compression [13]. Nonetheless, these methods require strict linearity of the defect signals. Thus, [PERSON] et al. [14] used a time-domain phase analysis method for detecting blind zone defects; however, the amplitude information was not retained during the binary processing. [PERSON] [15] designed a low-pass digital filter to filter out signals unrelated to defects, but it required horizontal movement of the defect signal and reference signal to align the peaks for defect detection. Also, [PERSON] et al. [2] used a pulse-echo transverse wave backscatter measurement to detect near-surface defects with sub-wavelength. [PERSON] et al. [16] employed intrinsic time-scale decomposition to decompose an ultrasonic signal into proper rotation components and a monotone trend signal. These components were then combined with a genetic algorithm-optimization support vector machine (GA-SVM) to allow for quantitative testing of near-surface defects. [PERSON] et al. [17] used the analytic signal and its instantaneous parameters to suppress the front- and back-surface reflections of the ultrasonic echoes through response subtraction and substitution. These methods effectively demonstrate the potential of signal processing to extract useful information regarding near-surface defects. Although synchro-squeezing transform (SST) is a promising signal processing technique, it has not been extensively employed for ultrasonic defect detection. Inspired by the aforementioned studies, the synchro-squeezing transform (SST) [18] was introduced to detect and locate near-surface defects in this paper. However, it should be noted that like prior studies, this method is unable to directly identify the specific type of defect. A convolutional neural network (CNN) [19] is a classifier that contains multiple layers and adapts the filters by learning the information of the signal. The ability of CNN for image object detection has been verified in many aspects, such as fabric defect detection [20], wood defect detection [21], surface scratch defect detection during sheet metal forming [22], and surface defect detection of engine parts [23].