## 3 AI Techniques and Their Applications
### Deep Learning Techniques
#### 3.2.1 Feature-Based Classification and Clustering  
Hyperbola detection is one of the most extensively studied tasks in GPR analysis, as it signifies the presence of buried point-like or elongated objects such as pipes, rebar, and cables. Traditionally, these hyperbolic signatures have served as key indicators in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Input Features** & **Typical Application** & **Advantages** & **Limitations** \\ \hline SVM & Hyperbola shape & Target detection & High precision & Sensitive to noise \\ Random forest & Statistical feature & Material classification & Robust to overfitting & Requires parameter tuning \\ KNN & Distance-based metrics & Clustering & Conceptually simple & Low accuracy on complex data \\ Naive Bayes & Intensity-based & Anomaly detection & Fast and lightweight & Poor generalization ability \\ \hline \hline \end{tabular}
\end{table}
Table 3: Traditional Machine Learning Algorithms used with GPR.  
subsurface imaging due to the distinct geometric patterns that emerge when radar waves reflect off small, discrete targets. With the advent of deep learning, CNNs have been increasingly employed for this task, offering robust performance in identifying hyperbolic features amidst noisy and complex backgrounds. These CNNs are typically trained on large, annotated datasets, enabling them to learn the spatial and structural characteristics of hyperbolic patterns with high precision. To effectively perform hyperbola detection, researchers have adapted well-established object detection frameworks originally developed for natural image processing, including YOLO, Faster R-CNN, and the Single Shot MultiBox Detector (SSD). YOLO is known for its real-time detection capabilities--it treats detection as a regression problem, allowing it to simultaneously predict bounding boxes and class probabilities. Faster R-CNN offers high accuracy by using a region proposal network to generate candidate object regions before classification, making it suitable for high-resolution GPR data. Meanwhile, SSD balances speed and accuracy by predicting bounding boxes at multiple scales from feature maps, allowing the efficient detection of hyperbolas of varying sizes. The adaptation of these architectures to GPR data has significantly advanced the automation and reliability of hyperbola detection, facilitating the faster and more objective interpretation of subsurface features, which is crucial for applications in utility mapping, infrastructure assessment, and archaeological exploration [40; 41].  
These models are capable of detecting multiple hyperbolas within a single frame, even in high-noise environments, making them highly effective for practical applications. To enhance the accuracy and reliability of their predictions, researchers have integrated various post-processing techniques, such as non-maximum suppression and ellipse fitting. These refinements help reduce false positives and improve the precision of object localization. As a result, such models have been successfully applied across a range of domains, including utility mapping in complex urban infrastructure and the detection of reinforcement bars during concrete inspection. Their robustness under challenging conditions and adaptability to different use cases underscore their growing importance in GPR interpretation, where efficient and accurate feature recognition is essential for both construction and maintenance workflows [42; 43; 44].