# A Fast Maritime Target Identification Algorithm for Offshore Ship Detection
## 2 Related Work  
Maritime rights and interests have once again become a focus of the world since the beginning of the 21 st century, and the strategic position of coastal nations has been promoted like never before. Coastal countries constantly improve their ability to protect their unique marine interests and rights. They also aim to protect the coastal and marine ecosystems and further develop the marine economy. Although technology has progressively become mature and the speed of recognition has gradually increased, the two-step algorithm fails to achieve real-time because the model is divided into multiple stages and calculates work redundantly. To resolve this problem, scholars have introduced the YOLO series [10; 11], SSD, and other single-stage target detection methods; however, these approaches listed above improved the speed while reducing the accuracy. Marine target detection work differs from land-based recognition in that maritime vehicles are constrained by waves, which impact vessel behavior [12], including six sorts of activities [13], such as surging, swaying, heaving, rolling, pitching, and yawing. The semantic data was markedly deficient. Thus, CNN-based target detection methods are available for maritime target recognition in natural scenes. CNN was constructed by several various layers, with the network being trained to understand the relationship between the data, and the model describes the mapping relationship between the input and the output data. Both traditional and deep learning target detection methods together constitute the current dominant target detection methods. Many scholars have conducted research to obtain the best possible precision and speed in balance. [PERSON] et al. [14] proposed a discrete wavelet transform-based method for ship detection that relied on statistical behavior differences between ships and surrounding sea regions to help in the interpretation of visual data, resulting in more reliable detection. [PERSON] et al. [15] introduced the You Only Look Once version 2 (YOLO V2) approach to recognizing ships in SAR images with high accuracy to overcome the computationally costly accuracy problem. [PERSON] et al. [16] used a combination of a modified Generative Adversarial Network (GAN) and a CNN-based detection method to achieve the accurate detection of small vessels. Contemporary technologies and models have limitations, such as the inability to recognize closed-range objects. Other scholars have performed outstanding work and contributed to the implementation of neural network algorithmic methods by migration to solve practical problems in different fields. [PERSON] et al. [17] analyze target detection algorithms (Faster R-CNN, R-FCN, SSD, and YOLO V2), combined with some extractors (Resnet V1 50, Inception V2, darknet-19, etc.) to improve and adapt the traffic sign detection problem through migration learning. It is worth noting that ResNet's network structure has been introduced, resulting in exceptional performance in fields including non-stationary GW signal detection [18], Magnetic Resonance Imaging identification [19], the CT image recognition [20], and agricultural image recognition [21]. Feature Enrichment Object Detection (FEOD) framework with weak segmentation Loss based on CNN is proposed by [PERSON] et al. [22], the Focal Loss function is introduced to improve the algorithm performance of the algorithm. [PERSON] et al. [23] proposed a new decentralized adaptive neural network control method, using RBF neural networks, to deal with unknown nonlinear functions to construct a tracking controller. A new adaptive neural network control method was proposed by [PERSON] et al. [24] for uncertain multiple-input multiple-output (MIMO) nonlinear time-lag systems. To address the problem of Slow Feature Discriminant Analysis (SFDA) which cannot fully use discriminatory power for classification, [PERSON] et al. [25] proposed a feature extraction method called Adaptive Slow Feature Discriminant Analysis (ASFDA).