## 1 Introduction  
Ground Penetrating Radar (GPR), as a non-destructive, efficient, and convenient geophysical detection instrument, has a wide variety of applications in urban infrastructure inspection [1], urban road internal disease detection [2], urban underground pipeline probing [3], rebar recognition for tunnel secondary lining [4], and identifying surrounding cracked rocks of underground caverns [5]. In different application scenarios, the ROIs of reflection hyperbola are difficult to identify quickly and accurately due to the different acquisition environments. In addition, strong noise interference will cover up the hyperbolic morphological characteristics in GPR images, making them difficult to visually discern. Currently, image segmentation offers a solution by delineating specific regions within an image, enabling pixels with similar features to be grouped together. This segmentation step typically precedes image target detection, providing shape, and structural features necessary for target identification [6]. Through image segmentation, specific target regions within the image can be identified and detected, allowing for the extraction of crucial regions for diagnosis. In the GPR profile, the ROIs typically appear as hyperbolic regions, indicating potential burial sites for the target [7]. When GPR detects underground targets along the surface, the transmitting antenna emits high-frequency electromagnetic waves into the subsurface. If there is a dielectric contrast between the subsurface target and the background medium, a reflected wave is captured by the receiving antenna, forming the GPR records [8]. Ideally, GPR images would only contain the airway near the surface if there were no dielectric variations in the subsurface, without reflections from the deep underground targets. However, single-point anomalies or cylindrical scattering in the subsurface, such as pipes, cables, isolated rocks and voids, exhibit distinct hyperbolic characteristics in the GPR record [9]. As typically only a relatively small area of the radargram contains useful reflections corresponding to targets, segmenting and detecting these hyperbolic regions is crucial to reducing the dataset dimensions, thus alleviating the computational burden during later processing [10]. Currently, many researchers have adopted various signal and image processing methods to detect hyperbolic ROIs. In the signal feature transform domain, [PERSON] et al. [11] developed a coherence analysis (CA) framework for extracting GPR diffractions, potentially improving imaging resolution and thereby facilitating a delicate GPR data processing. [PERSON] et al. [12] incorporated a local plane-wave destruction filter with an online dictionary learning algorithm for individually separation and imaging of GPR diffractions, particularly effective for subsurface small-scale buried targets. [PERSON] et al. [13] employed wavelet denoising and gamma transformation to suppress the complex background noise and build a multi-scale image brightness and orientation feature pyramid model to enhance abnormal ROI features. In terms of image processing techniques, [PERSON] et al. [14] explored the histogram of oriented gradients (HOG) for landmine detection in GPR data, optimally extracting locations of interest for target identification and classification. [PERSON] et al. [15] calculated density, threshold, and derivation values in GPR images to detect profiles with anomalies and visualize them. [PERSON] et al. [16] applied the column-connection clustering (C3) algorithm to cluster ROIs after thresholding from the background of GPR images, identifying hyperbolic signatures. [PERSON] et al. [17] applied the variance filter to the raw GPR image, followed by iterative segmentation based on selected ranges, facilitating feature display in water leak GPR images to highlight the anomaly area of interest.