Resumen
Ship abnormal behavior detection is an essential part of maritime supervision. It can assist maritime departments to conduct real-time supervision on a certain sea area, avoid ship risks, and improve the efficiency of sea area supervision. Given the problems of complex detection methods, poor detection effectiveness, and low detection accuracy, a Gated Recurrent Unit (GRU) was proposed for ship abnormal behavior detection. Under the premise of introducing the attention mechanism into a GRU, the optimal GRU structure parameters were obtained through the intelligent algorithm to perform deeper feature extraction and train the ship abnormal behavior based on the optimized GRU neural network, so as to realize the detection and recognition of the trajectory data to be measured. Finally, based on the public data set and the trajectory data of the inward and outward ports of ships issued by Nanjing Section, Jiangsu Maritime Bureau, the TensorFlow frame was used to establish an abnormal behavior detection model. The simulation results demonstrated that the abnormal behavior detection model shortened the abnormal detection time. The abnormal behavior detection model used in the detection of ship abnormal behavior enhanced the accuracy and stability of the abnormal behavior identification and verified the validity and superiority of this method.