# A Methodology Based on Deep Learning for Contact Detection in Radar Images
## 1 Introduction  
Today's application of radars has expanded considerably in both civil and military fields due to the progress in radar technology. Such is the case that state-of-the-art radars can be detected in all weather conditions during the day, night, and long distances [1]. The navigation radar provides visual output about the elements present in a navigation scenario, displaying the position and distance information for each element. However, the identification and location of targets of interest on the sea surface are affected by a high degree of variability in the radar imagery [2]. This is due to several factors, such as weather conditions, alterations in the height of sea waves, land portions, birds, and noise caused by electronic interference [3, 4]. Therefore, the search for a detection model that allows discarding unwanted echoes and detecting targets of interest with accuracy and low computational cost is a complex task, made even more so with commercial radars due to their limitations in terms of range, resolution, and reliability compared to military radars [5]. Nevertheless, detecting objects on the sea surface is of great interest for its application in civilian operations such as the timely rescue of vessels, maritime security, and the surveillance of the Peruvian coast [1; 6; 7; 8]. Many commercial radars use the adaptive algorithm CFAR (Constant False Alarm Rate) in its various variants to establish the detection threshold. This algorithm presents a non-zero false alarm rate (PFA) [9]. The noise power is estimated from neighboring cells, and a dynamic detection threshold is set to match the data. The most used detector CFAR is the averaging of neighboring cells, calculated at a constant CUT (Cell Under Test) distance [10; 11; 12]. Experimentally obtained data from the radar installed on the vessel show a false negative rate of 0.05 and a false alarm rate of 0.10, especially in the images of regions containing sea and land in navigation parallel to the coast. To decrease this effect, the CFAR result is combined with other algorithms; multihypotheses and filtering with navigation maps are applied, significantly increasing the computational burden. In addition, for safety reasons, it is preferred to adjust. The constant CFAR parameters are used to reduce the false negative rate. However, this implies increasing false alarms, an effect that is compensated for by predictive algorithms combining the results of other antenna spins, inevitably increasing the computational cost [13; 14]. On the other hand, modern methods based on deep learning algorithms are abstract feature-based methods that detect ships using features such as shadows, contours, and textures, which are extracted through deep convolutional neural networks (CNNs), achieving better detection performance than the traditional method [15]. This second group of methods can be classified into two-stage and one-stage detectors. Two-stage detectors are represented by the series of region-based convolutional neural networks (R-CNNs), which later evolved into Fast R-CNN and Faster R-CNN [16; 17]. Faster R-CNN is distinguished by its two-stage process, where it first generates region proposals and then classifies those regions into object categories; due to the unification of the regional proposal and detection networks in a single model, it has turned out to be faster than its predecessors [18; 19]. These models have the advantage of recognizing different types of objects more accurately, overcoming the problem of multiscale and multiscene detection [20; 21; 22]. Despite their complexity, two-stage detectors are favored for their high accuracy and robustness in object detection tasks [23; 24; 25]. Indeed, the single-stage detectors such as Single-Shot Multibox Detector (SSD) [26] and You Only Look Once (YOLO) [27] work much faster than two-stage detectors [28; 29].