# A Methodology Based on Deep Learning for Contact Detection in Radar Images
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Backbone**} & \multicolumn{3}{c}{**Accuracy**} & \multirow{2}{*}{**Processing Time**} \\ \cline{3-3} \cline{5-6}  & & **Precision** & **Recall** & **F1 Score** & **mAP** & \\ \hline Faster R-CNN & ResNet34 & 0.90 & 0.68 & 0.77 & 0.21 & 0:50:41 \\  & ResNet50 & 0.82 & 0.83 & 0.82 & 0.23 & 00:53:20 \\  & ResNet101 & 0.85 & 0.85 & 0.85 & 0.27 & 01:25:39 \\ \hline Retina Net & ResNet34 & 0.08 & 0.01 & 0.01 & 0.00 & 00:42:22 \\  & ResNet50 & 0 & 0 & 0 & 0.00 & 00:57:52 \\  & ResNet101 & 0.74 & 0.74 & 0.74 & 0.14 & 01:14:46 \\ \hline SSD & ResNet34 & 0.30 & 0.09 & 0.14 & 0.01 & 00:28:32 \\  & ResNet50 & 0.44 & 0.24 & 0.31 & 0.02 & 00:43:19 \\  & ResNet101 & 0.49 & 0.31 & 0.38 & 0.03 & 01:00:25 \\ \hline \hline \end{tabular} Source: Adapted from [PERSON] (2024) [36].  
\end{table} 
Table 1: Results of marine vessels detection models (100 epochs). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Object Detector** & **CNN Architecture** & **Precision** & **Recall** & **F1 Score** \\ \hline Faster R-CNN & ResNet50 & 0.692 & 0.819 & 0.749 \\  & ResNet101 & 0.680 & 0.777 & 0.723 \\  & ResNet152 & 0.655 & 0.774 & 0.709 \\ \hline YOLOv3 & DarkNet53 & 0.592 & 0.659 & 0.619 \\ \hline SSD & ResNet50 & 0.559 & 0.531 & 0.543 \\  & ResNet101 & 0.547 & 0.513 & 0.527 \\ \hline Faster R-CNN & ResNet34 & 0.440 & 0.426 & 0.423 \\ \hline \hline \end{tabular} Source: Adapted from [PERSON] et al. (2022) [37].  
\end{table} 
Table 2: Results of shrub species detection accuracy for different deep learning methods. Peruvian Navy vessels. Therefore, our choice is mainly focused on detection accuracy over real-time processing. In this study, a methodology is designed for the detection of vessels of different shapes and sizes on the Peruvian coast, using original radar images owned by the Peruvian Navy. The present work estimates the performance, in terms of accuracy and speed, of new detection models based on deep learning. While object detection is a well-established field, this study is novel, as it examines this new approach for the first time. It is conducted on one of the most important ports of Peru, called Callao. This article describes the complete methodology for contact detection. The convolution techniques applied and the parameters defined for the preprocessing and enhancement of radar images are detailed. In the second phase, the artificial intelligence techniques used to obtain cuts of objects of interest in an automated manner and the criteria that were considered for more efficient labeling are described. Subsequently, the design of the experiments and training using deep learning algorithms based on the Faster R-CNN architecture is described. The hyperparameter tuning values are specified below. Finally, the discrimination process is detailed according to the criteria defined by the radar expert to obtain the persistent objects called plots. The experiments with the Faster R-CNN model demonstrate favorable results with an average F1 score of 0.95, which demonstrates the model's ability to recover more objects and with high precision. The rest of the paper is organized as follows: Section 2 presents the detection system in the Peruvian Navy; Section 3 describes the methodology; Section 4 presents the experiments and the analysis of the results; Section 4 presents future work; and Section 5 presents the conclusions.