# A Methodology Based on Deep Learning for Contact Detection in Radar Images
## 5 Conclusions  
After the analysis of the results, the following statements can be made:  
* The detection model yields a cost function value equal to 0.0372, a recall rate of 94.5%, and an accuracy rate of 95.1%.
* The detection time is favorable, equivalent to 4.513 s per radar turn. Considering the implementation of the algorithm in an integrated system with a pool of parallel processors, it is feasible to improve the detection time further.
* The modification of weight initialization (\(\mu=0;\sigma=0.05\)) and the ADAM optimizer are the determining hyperparameters in improving the training, and consequently the object detection model. The IoU and batch-size hyperparameters influence but are not decisive in the improvement of the performance of the object detection model.
* The cross-validation method is independent in handling partitions and eliminates bias by randomly selecting samples for training and validation datasets. In addition, it allows the best models to be chosen based on rigorous verification that guarantees their implementation in real projects.
* Automatic cropping makes it easy to obtain more precise cuts of objects quickly. These cuts meet the characteristics of the intensity and dimensions of the objects of interest.
* Displaying objects of interest in three consecutive turns allows for a faster and more reliable labeling process. The developed interface increases the efficiency of object labeling.
* Given the results, with the proposed methodology, it is possible to generate a ship detection model that works well despite the noise and clutter reflected in the radar images. The detection model obtained is valid for use with commercial radar images.
* This work is the beginning of future projects with other deep learning algorithms and using an advanced computing infrastructure.
* The findings may be particularly applicable to other coastal regions, where advanced remote sensing applications have not yet been widely explored.  
[PERSON], [PERSON] and [PERSON] proposed the concept of this research. [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the methodology. [PERSON], [PERSON] and [PERSON] contributed to designing and constructing proprietary software to automatically crop objects of interest and the rule-based labeling interface for radar experts. [PERSON] and [PERSON] contributed to the improvement of data and experiments. [PERSON] and [PERSON] contributed to the validation of the results. [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper. [PERSON] contributed to the translation of the paper and review of the content. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by CONCYTEC-World Bank Project, through its executing unit, Fondo Nacional de Desarrollo Cientifico, Tecnologico (FONDECYT) within the framework of the call E033-2018-01-BM of Contract No. 06-2018-FONDECYT/BM, executed as part of the PhD program in Engineering with a major in Automation, Control, and Process Optimization, developed in the Automatic Control Systems Laboratory of the University of Piura, Peru.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The radar images have been provided by the Peruvian Navy and belong to the scenario of interest (Callao-Per\(\acute{\text{a}}\)). A limited set of images is available at [[https://www.kaggle.com/datasets/pedrorottaudep/radar-dataset-laps](https://www.kaggle.com/datasets/pedrorottaudep/radar-dataset-laps)]([https://www.kaggle.com/datasets/pedrorottaudep/radar-dataset-laps](https://www.kaggle.com/datasets/pedrorottaudep/radar-dataset-laps)) (accessed on 3 September 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest.