# A Fast Maritime Target Identification Algorithm for Offshore Ship Detection
## 2 Related Work
A fast face detection method based on convolutional neural networks to extract Discriminative Complete Features (DCFs) was proposed by [PERSON] et al. [26] it detaches from image pyramids for multiscale feature extraction and improves detection efficiency. [PERSON] et al. [27] establish a multitask model based on the YOLO v3 model with Spatial Temporal Graph Convolutional Networks Long Short-Term Memory to design a framework for robot-human interaction for judgment of human intent. To resolve the real-time problem of recognition, [PERSON] et al. [28] introduce an attention mechanism and propose a new attention mechanism-based real-time detection method for traffic police, which is robust. [PERSON] et al. [29] proposed a multiscale feature fusion method based on bidirectional feature fusion, named Adaptive Multiscale Feature (AMF), which improves the ability to express multiscale features in backbone networks. Additionally, scholars have been working on meaningful improvements based on them and making assistance for further enhancement of maritime target recognition. The integrated classifier MLP-CNN was proposed by [PERSON] et al. [30] to exploit the complementary results of CNN based on deep spatial feature representation and MLP based on spectral recognition to compensate for the limitations of object boundary delineation and loss of details of fine spatial resolution of CNN due to the use of convolutional filters. [PERSON] et al. [31] proposed a neural network with a hybrid CNN and multilayer perceptron algorithm for image classification, which detected target pixels based on the statistical information of adjacent pixels, trained with real SAR images from Sentinel-1 and RADARSAT-2 satellites, and obtained good performance. For the pre-processed data, [PERSON] et al. [32] employed a support vector machine (SVM) classifier to classify the ships by assessing the feature vectors by calculating the average of kernel density estimates, three structural features, and the average backward scattering coefficients. [PERSON] et al. [33] proposed a segmentation-based constant false alarm rate (CFAR) detection algorithm for multi-looked intensity SAR images, which solves the problems related to the target detection accuracy of the non-uniform marine cluster environment, and the detection scheme obtains good robustness on real Radarsat-2 MLI SAR images. Meanwhile, a robust CFAR detector based on truncation statistics was proposed by [PERSON] et al. [34] for single- and multi-intensity synthetic aperture radar data to improve the target detection performance in high-density cases. [PERSON] et al. [35] applied a probabilistic graphical model to develop a two-stage target recognition framework that combines the advantages of different SAR image feature representations and differentially learned graphical models to improve recognition rates by experimenting on a reference moving and stationary target capture and recognition dataset. In order to tackle the collision avoidance problem for USVs in complex scenarios, [PERSON] et al. [36] suggested a negotiation process to accomplish successful collision avoidance for USVs in complicated conditions. [PERSON] et al. [1] suggested employing the EfficientDet model for maritime ship detection and defined simple or complex settings with a positive recognition rate in the above circumstances, which provides an important reference for maritime security defense. For USV systems with communication delays, external interference, and other issues, [PERSON] et al. [37] suggested an event-triggered communication strategy. Additionally, an event-based switched USV control system is proposed, and the simulation results show that the proposed co-design process is effective. Traditional target detection methods include different color specificities of own color space models and manual design to extract features.