# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 1 Introduction
The first type of target detectors are based on region recommendations, such as Region-based Convolutional Networks (R-CNN) [13], Spatial Pyramid Pooling Network (SPP-NET) [14], Fast R-CNN [15], and Faster R-CNN [16]. This type of detector first uses regional recommendations to generate candidate targets, and then uses the convolution neural network for processing. Its accuracy is high, but it does not meet the requirements for real-time processing. The second type of target detectors are regression-based, such as You Only Look Once (YOLO) [17; 18; 19], Single Shot Multibox Detector (SSD) [20], etc. These methods treat the detection problem as a regression and directly predict target positions and categories. This type of detector has a fast processing speed, but low accuracy. [PERSON] et al. [2] applied R-CNN to large scene SAR target recognition, which can detect objects while recognizing their classes thanks to its regression method and the sharing network structure. [PERSON] et al. [21] used RetinaNet with feature pyramid networks (FPN) and focal loss to detect ships, and achieved high accuracy multi-scale ship detection. [PERSON] et al. [22] used the Faster R-CNN method to conduct the initial ship detection and then applied CFAR to obtain the final results. The challenge of on-board ship detection of lightweight SAR satellite is to achieve the accurate and efficient detection of targets under the constraints of the limited memory and computing power of the satellite processing platform. Most of the ship detection methods use high-power GPUs on the ground to detect ships, and these network models are always large in size and complex computations. However, the special hardware used for lightweight satellites is different from the mature technology available in ground data centers. Therefore, these highly complex network models cannot be used to complete real-time detection missions with the limited on-board memory and computing power. To solve the above problem, we propose an on-board ship detection scheme that combines the traditional CFAR algorithm and lightweight deep learning to achieve efficient on-board ship detection. Firstly, this method is based on geographic prior experience and regional growth methods to realize sea-land segmentation. Sea-land segmentation can reduce the interference of the land and improve detection efficiency. Secondly, the K-distribution is used to model sea clutter in images, and the CFAR algorithm is used for fast rough detection. Subsequently, the initial detection results are used as input for the YOLOv4-tiny network to refine the detection results. Finally, through a reasonable hardware acceleration strategy, the entire algorithm was ported into a Jetson TX1 GPU and achieved an acceptable algorithm speed. This method only uses the preliminary extraction results as the input for the YOLOv4-tiny network, which greatly reduces the computation power required. The main difference between our method and the vast majority of the SAR automatic target recognition approaches is that we consider an end-to-end workflow, in which manually extracting samples for target classification is not required. In addition, our algorithm can directly generate the category probability and position coordinate value of the object, and obtain the final detection result after a single detection, so it has a faster detection speed. When combined with the hardware acceleration strategy, our method achieved high accuracy and efficiency on the ground verification platform. The main contributions of this paper are as follows:  
1. In order to eliminate the influence of land, we first remove the land from the image, which greatly reduces the computation load of the algorithm. Subsequently, we use the CFAR algorithm that is based on K-distribution to extract ship targets. 2. We input the CFAR detection results into the YOLOv4-tiny network for further ship detection using its efficient global context information extraction capability. 3. We combine each part of the algorithm organically in the Jetson TX1 platform.