**2. Related Work**

We will discuss deep learning and embedded device supported object recognition as ESDR-DL is in principle an embedded deep learning approach, we will also discuss vehicle recognition as ship is also a kind of vehicle.

#### *2.1. Deep Learning*

The concept of deep learning originates from the study of artificial neural networks, proposed by Hinton et al. [6]. Deep learning have made remarkable achievements in the field of image processing, especially for object detection. SSD is a typical one stage detector proposed in [7], which processes images in a single network, and and has good efficiency and accuracy. Faster R-CNN [8] is a two-stage detector, which uses RPN (Region Proposal Network) to produce high-quality region proposals and then detect them with Fast R-CNN [9].

Redmon presents a single neural network named YOLO, which abandons anchor boxes, and predicts bounding boxes and class probabilities directly from a full image in one evaluation [10]. YOLO considers object detection as a regression problem to predict bounding boxes and class probabilities. It can be optimized as end-to-end directly with good detection performance. Fast YOLO can process 155 frames per second. Compared with other state-of-the-art detection algorithms, YOLO makes more localization errors.

YOLOV2 [11] is based on YOLO [10]. YOLOV2 removes the fully connected layers from YOLO and uses anchor boxes to predict bounding boxes. The YOLOV2 model can run with various image sizes, and it is easy to make a trade-off between speed and accuracy. YOLOV2 is faster than YOLO, which can process 200 frames per second with the Tiny model. Table 1 shows the performance of these algorithms.


**Table 1.** The performance of the algorithms.
