# A Methodology Based on Deep Learning for Contact Detection in Radar Images
## 1 Introduction
However, they compromise on accuracy, being insufficient when it comes to small objects [30; 31], images with a lot of noise with scenarios with many background clouds, low resolution data where contour processing must be processed, and gray level distributions to distinguish the target [32]. While YOLO and SSD single-stage detectors excel in providing real-time processing and fast inspection capabilities, these may not always offer precise location for specific objects of interest [33; 34]. Lately, several research works have been carried out to mitigate the effects of adverse visual conditions on object detection and demonstrate that YOLO architectures may be best suited for object detection under adverse visual conditions, such as rain, sunset, day or night. However, it should be noted that R-CNN models, despite their significant computational costs, have proved to be the most suitable for nighttime detection [35]. Faster R-CNN demonstrates its exceptional precision when locating objects at sea in conditions of very low visibility. The recent research of [36] entitled \"Faster R-CNN, RetinaNet and Single Shot Detector in different ResNet backbones for marine vessel detection using cross polarization C-band SAR imagery\" confirms that Faster R-CNN and SSD show different capabilities in marine vessel detection. Faster R-CNN stands out as the best detector. This study shows that as the complexity of the ResNet backbones and the number of epochs in creases, the accuracy of the model improves (100 epochs: Faster R-CNN = 0.67-0.70 mean F1 score; RetinaNet = 0.00-0.68 mean F1 score; SSD = 0.18-0.34 mean F1 score). Regarding the processing time, Faster R-CNN with different ResNet backbones takes longer to train for 100 epochs (1 h and 3 min) compared to other detectors (RetinaNet = 58 min; SSD = 44 min). Table 1 shows some results on the accuracy and processing time of marine vessel detection models (100 epochs) extracted from the work of [36]. In another research work [37], three different object detectors were tested: one two-stage detector (Faster R-CNN) and two single-stage detectors (YOLO v3, SSD). This choice was made to make a comparison between the increased speed typically seen in single-stage approaches and increased accuracy often achieved by two-stage approaches. For all image resolutions, Faster R-CNN using ResNet-50, 101 and 152 achieved the three highest F1 scores, reporting a maximum F1 score of 0.82 on their shrub detection. Table 2 shows shrub species detection for different deep learning methods extracted from the work [37]. Based on the literature, it is shown that two-stage object detectors achieve higher accuracy at the expense of the processing speed. On the other hand, detectors with single-stage approaches, such as YOLO and SSD, are more suitable for real-time processing. The purpose of our research is to demonstrate that a new detection algorithm approach, based on deep learning, can improve the radar detection system currently used by the Peruvian Navy, CFAR. Faster R-CNN has been selected for other single-stage detectors for two reasons: (1) First is the deficiencies of the current CFAR model to monitor and detect marine vessels in scenarios with complex weather conditions of the Peruvian coast. The deficiencies are reflected in a false alarm rate of 0.10 and false negative rate of 0.05 in images of regions parallel to the beach. (2) Second is the radar infra