# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
## 1 Introduction
With the development of Commercial Off-The-Shelf (COTS) component, it has become an important way to reduce the cost and shorten the development cycle of micro-nano satellites, which has the advantages of low cost, superior performance, high integration and easy availability. For the past few  
Figure 1: The comparison between ground processing and on-board processing. The black line is the traditional ground processing flow, and the red line is the on-board processing flow. years, cheap, compact, low-power and high processing performance modules are emerged, such as Jetson TX1 and TX2 by NVIDIA Corporation. These modules are very adequate for on-board processing of micro-nano satellites. In the past decade, some traditional methods have been proposed for ship detection [9; 10; 11; 12; 13]. [PERSON] et al. [9] applied shape and texture features to extract the region of interest, and then a semi-supervised hierarchical classification was adopted to eliminate the false alarms. [PERSON] et al. [10] used anomaly detector and local shape feature to detect ships in a \"coarse-to-fine\" manner. [PERSON] et al. [11] developed an unsupervised ship detection method based on saliency and S-HOG descriptor. [PERSON] et al. [12] proposed a detection algorithm based on saliency segmentation and the local binary pattern descriptor combined with ship structure. [PERSON] et al. [13] constructed a hierarchical model based on multi-scale saliency, rotation-invariant feature, and trainable Gaussian support vector machine classifier. Most of these methods are designed on image segmentation and hand-crafted features, which may have poor performance in complex background. In recent years, deep learning has achieved great success in computer vision [14; 15; 16; 17]. The development of deep learning technology makes it possible to detect ships with high accuracy and high processing speed. The on-board ship detection algorithm needs to detect as many targets as possible with limited resources and take both the detection accuracy and model size into consideration. However, most of the previous ship detection methods [18; 19; 20; 21; 22; 23; 24; 25; 26] detected ships on small image patches by high-power Graphic Processing Units (GPUs) on the ground, without considering the computing and storage resources for practical aerospace application. To solve the problems above, this paper presents an on-board ship detection scheme based on COTS component and deep learning, which can be used to achieve near real-time on-board processing by micro-nano satellite computing platform. In addition, a ground demonstration and verification system is built to verify the feasibility and effectiveness of our scheme. Experimental results show that the proposed scheme has a good application prospect in micro-nano satellites with limited power and computing resources. The main contributions of this paper are three-fold. 1. We design an on-board ship detection scheme based on deep learning and COTS component. Different from the traditional ground processing flow, our scheme directly distributes the target patches and valuable information to ground users from micro-nano satellites, which can increase the response and processing speed, application ability, and the data utilization rate of micro-nano satellites. 2. We adopt the strategies of image cropping, square anchor, global average pooling, and BL-NMS to extract the target patches and information from remote sensing images. Compared to the previous ship detection methods, our method can achieve higher detection accuracy with a light weight model size, which meets the requirement of practical aerospace application with limited computational and storage resources. 3. We collect a well-annotated dataset from Google Earth, which includes the binary mask, bounding box, and squared bounding box of each ship. The performance of the proposed scheme tested by a ground demonstration and verification system demonstrates that our scheme has a promising application prospect in on-board intelligence processing.