## 3 The Application of Conventional ML Algorithms
### Artificial Neural Network (ANN)  
An artificial neural network (ANN) is a mathematical model or computational model that imitates the structure and function of a biological neural network and is used to estimate or approximate functions. In most cases, the artificial neural network can change the internal structure on the basis of external information and it is an adaptive system, that is, it has a learning function. Like other machine learning methods, neural networks have been used to solve problems that are difficult to solve by rule-based programing. ANN hasa distinguishable structure that is composed of multiple layers, and the conventional ANN usually consists of input layer, hidden layer, and output layer. The structure of a single neuron is shown in Figure 7; it can be seen that the function of a single neuron is to obtain a scalar result through a nonlinear transfer function after obtaining the inner product of the input vector and the weight vector. Its function is to divide an n-dimensional vector space into two parts with a hyperplane (called the judgment boundary) and then, arbitrarily given an input vector, the neuron can determine which side of the hyperplane the vector is on, which actually completes the classification function. A single-layer neural network can be obtained by combining a limited number of single neurons. The schematic diagram of its structure is shown in Figure 8; this is the most basic form of neuron network. The input vectors of all neurons are the same vector, and each input vector will also be connected to all neurons. Since each neuron produces a scalar result, the output of a single layer of neurons is a vector whose dimension is equal to the number of neurons. As CNN and other popular deep learning methods have structural differentiation from conventional ANN, their application in GPR classification will be discussed in Section 3. In 2000, AI-Nuaimy extracted the spectral features of GPR scan, and then divided these feature vectors into training and testing set [29]. They proposed an NN to learn the instances in the training sets to construct a rule for the labelling of regions with or without buried objects. Another study in the same year used shape filtering to extract the hyperbolic signatures from the binarized B-scan images and developed a two-layer feedforward network classifier [30]. By combining simple data preprocessing rules and neural network image interpretation, a robust pipeline feature space detection method is proposed. Satisfactory results were obtained through experiments. [PERSON] et al. proposed a complex recognition system of landmines and morphological shared-weight neural networks (MSNN) were utilized in this system, of which the input was down-track slices of B-scan data [31]. The reason for using this network is its ability to simultaneously learn feature extraction and classification parameters, further improving efficiency. The method based on fuzzy set-based fusion of the information sources results in significant reductions in false rates. In 2005, a multi-layer perceptron (MLP) network was applied to the location of steel reinforcement in rebar [32]. The reason for choosing MLP is that it has low complexity in terms of node count, network training, and application and the data can be preprocessed  
Figure 8: Schematic diagram of a single-layer neural network structure. Figure 7: Schematic diagram of a single neuron structure. before MLP network analysis. The main reason is that this network could learn with the simplified dataset that had been processed by edge detection. Then, in 2010, a standard multilayer neural network trained by trapezoidal image sections with the apex and one branch of the hyperbola was applied to the automatic detection of reflection hyperbolas in B-scan images [33]. The neurons are no longer fully connected to the input vector; each one rather receives just a small area of the presented image section. Adjacent neurons thereby are responsible for adjacent areas, which are freely configurable and may overlap arbitrarily.