## 1 Introduction
However, due to the complex detection environment and reflection clutter, image processing technologies based on threshold and clustering methods are effective for single anomaly processing, but inadequate for detecting multi-object reflection anomalies, resulting in low detection efficiency. The diversification of GPR acquisition scenarios necessitates the quick processing of large volumes of raw GPR data. Typically, only a small fraction of collected data represent useful information (i.e., target data), while the majority is considered redundant in a typical GPR survey [18]. Simplifying images and focusing on abnormal ROIs can be achieved through image binarization, a common image processing technique separating foreground objects from the background. This simplification reduces data volume, highlights target contours, and lays the groundwork for future target detection and identification [19]. Determining an appropriate threshold is crucial for proper imaging in GPR. A higher threshold may exclude important detail, while a lower threshold may include noise, leading to clutter [20]. Several scholars have conducted relevant studies addressing these considerations. [PERSON] et al. [21] processed the radargrams by edge extraction using the Sobel operator, evaluating the randomized Hough transform (RHT) algorithm for tree root recognition in GPR images. [PERSON] et al. [22] utilized the Canny edge detector as the initial stage of APEX (Automated Point Extraction from Radargrams) for automated hyperbolic reflection detection. [PERSON] et al. [23] implemented Otsu thresholding and Hough transformation for soil plane extraction in GPR B scans. [PERSON] et al. [24] employed the Canny edge detection method for hyperbolic reflection detection as a processing step, reducing bulk image data to representative points and lines. Traditional image segmentation methods have limitations, including applicability to simple image models, sensitivity to complex backgrounds and noise, and a lack of self-adaptation to images, resulting in poor robustness. Consequently, traditional methods struggle to yield satisfactory results in various applications of GPR B scan data processing, hindering efficient analysis of GPR profiles. While GPR data collection efficiency is high, processing and interpreting abnormal regions pose a bottleneck in technology development. Manual analysis remains the primary method for interpreting GPR images, relying on expert knowledge and being relatively slow. Hence, enhancing abnormal region recognition efficiency in GPR detection and achieving intelligent extraction of abnormal regions are crucial research issues. Recently, deep learning has achieved remarkable success in natural image segmentation and detection. Efforts in GPR image processing based on deep convolutional networks aim to improve hyperbolic ROI detection efficiency. Several studies have explored deep learning methods for various tasks in GPR analysis. [PERSON] et al. [25] proposed an algorithm for landmine detection using convolutional neural networks to analyze GPR B-scan images. They evaluated all patches of the B-scan image and detected objects by thresholding values. [PERSON] et al. [26] introduced a landmine detection method based on convolutional autoencoders applied to GPR B-scans. This method showed promising performance compared to previous CNN-based approaches. [PERSON] et al. [27] presented a Generative Adversarial Network (GAN)-based model to generate realistic GPR B-scan data. They also developed a single-stage object detection model using deep learning to automatically detect hyperbolas in GPR B-scan images. [PERSON] et al. [28] proposed a method based on residual CNN and Bi-LSTM models for predicting GPR type, soil type, and scanning frequency automatically. This method demonstrated high performance in specific GPR image analysis tasks. [PERSON] et al. [29] utilized a Single Shot Multibox Detector (SSD) model to identify ROIs containing hyperbolas in GPR images.