*2.2. Vehicle Identification*

There are many state-of-the-art algorithms that can be used for vehicle detection, such as RCNN [9] YOLO [10], which have high real-time performance, but the accuracy is not high for ship recognition. In [12], Wang proposed a vehicle real-time detection algorithm based on YOLOv2. It optimized important parameters of the model, and improved the number and size of anchors in the model, which can achieve both real-time and high accuracy for vehicle detection. It tested by a home-made dataset, which showed higher accuracy and ran faster than YOLOv2 [11] and RCNN. However, the real-time performance is based on high-performance equipment, which is not suitable for us.

Plate recognition is the most typical application for vehicle identification. Liu et al. [13] proposed CogniMem, which used a neural-network chip to recognize license plates. The CogniMem combined a video image processing module with a neural network module by using an equalized image processing algorithm and network classification algorithm. It contained processes of license location, character segmentation and character recognition. CogniMem can recognize car plates with low error; however, it required that the plates have a fixed character position and limited character type and numbers. Lin [14] proposed a method named ALPR to detect and recognize the characters in the plate region of an image. The approach is not applicable to the situation in which new targets emerge that are not annotated in its database.

#### *2.3. Embedded Object Recognition*

Embedded image processing has been attracting a lot of efforts. In [15], Arth et al. designed a full-featured license plate detection and recognition method using DSP. The processing core is a single Texas Instruments fixed point DSP with 1 MB RAM. Additionally, a slower SDRAM memory block of 16 MB exists. It can achieve real-time performance. In addition, Kamat and Ganesan [16] implemented a license plate detection system on a DSP using the Hough transform. Kang et al. [17] implemented a vehicle tracking and license plate recognition system on a PDA. An FPGA was used by Bellas et al. [18] to speed up parts of their license plate recognition system.

There was research that ran the Fast R-CNN on Jetson TK1 platform [19]. Although additional modifications on the Fast R-CNN were made to fit TK1, the detection speed was very low (1.85 frames per second-fps). The work in [20] ran a seven-layer CNN on TDA3x SoC for object classification, and the overall system performance was 15 fps. Therefore, a powerful software/hardware platform is needed to support efficient embedded deep learning based real-time video processing.

#### **3. Designing a Recognition Neural Network-DCNet**

DCNet is a two-stage network that consists of a DNet and a CNet as shown in Figure 1. DNet is a fully convolutional network [21] for ship parts detection including ship bow, cabin and stern. CNet is a classifier that can takes an image of any size and output a set of classification scores. We locate the ship parts from the DNet, and feed them into the CNet to get three classification scores (bow score, cabin score, stern score) of ship identify. Finally, a voter is used to recognize the ship as shown in Figure 1.
