# A Fast Maritime Target Identification Algorithm for Offshore Ship Detection
## 1 Introduction  
The maritime environment and international environment are becoming increasingly complex and changing, resulting in a rise in the type of vessels. The diversification of maritime targets, including warships, fishing vessels, cargo ships, etc., presents a great challenge for effective territorial water management [1]. Automatic ship detection algorithms are essential for effective territorial water surveillance. In a variety of sea conditions, this algorithm is capable of reliably recognizing arriving and leaving vessels. Currently, China's marine surveillance monitoring methods are divided into two groups based on how the information is obtained--active and passive approaches. The active methods get information from radar, video surveillance, remote sensing, underwater sonar, etc. On the other hand, the passive approach is usually used for the ship's automated identifying system.  
Recently, deep learning technology has made great progress in various fields. Additionally, Convolutional Neural Networks (CNN) have made outstanding contributions in plenty of areas [2], including image classification and recognition, video recognition, etc. The traditional approach to feature extraction required manual tasks. Because different types of targets have different dependent features, the manual extraction approach is ineffective. CNN [3] can extract probable features automatically, saving time on human extracted features. Neuro networks along with improvements in big data technology have led to a shift in maritime target identification from wasteful manual monitoring methods to deep learning automatic identification methods.  
Artificial intelligence advances are pushing the field of computer vision forward and providing practical answers to the problem of recognizing objects at sea. Many CNN-based recognition models have emerged, e.g., SSD [4], Faster RCNN [5], and YOLO [6]. There are two types of target detection algorithms--one-stage and two-stage ways. The end-to-end concept is used in the one-step method. After feature extraction, the image directly outputs the target class probability together with the location coordinate prediction frame, resulting in a faster detection. SSD and YOLO are typical representatives. The two-stage recognition procedure requires the identification of probable detection zones first and then classification of the objects. Two-stage profound network models are represented, e.g., RCNN series [7; 8] and SPPNet [9] have higher recognition accuracy and are independent of factors such as perspective, light, and cover.  
The main contributions of the article are as follows: (1) A novel detection algorithm, FMTI, is proposed for maritime target detection. (2) A multiscale feature fusion method is proposed to enrich the information of a single map. (3) FMTI can offer essential references for marine-related government functions to make their decisions.  
The rest of this paper is organized as follows. Section 2 shows the related work. Section 3 demonstrates the methodology and the implementation details of our FMTI algorithm. The performance metrics and the experimental results are presented in Section 4. Finally, Section 5 concludes the work and proposes future works.