# Use deep learning to improve the autonomous search and attack capabilities of torpedoes To use deep learning algorithms to enhance the autonomous ability of the torpedo, the following key aspects need to be considered： ## Feasible technical direction ### 1. Target detection and recognition -Use convolutional neural network (CNN) to process sonar/underwater acoustic data -Use real-time target detection algorithms such as YOLOv5/v8 to distinguish targets such as ships and submarines -Optimize the recognition accuracy of limited samples through migration learning ### 2. Environmental perception and modeling -Combine recurrent neural network (RNN) to analyze dynamic environmental data such as water flow and temperature -Use reinforcement learning to build an underwater environment model -Design attention mechanisms to deal with signal interference in complex marine environments ### 3. Path planning and decision-making -Deep Reinforcement learning (DRL) training optimal search strategy -Use dual Q network (DDQN) to balance exploration and utilization -Enhanced learning of multiple agents to achieve collaborative search and attack ## Implementation considerations -Algorithm lightweight: balance computing requirements with algorithm processing power -Robustness: deal with uncertainties such as signal interference and complex sea conditions -Simulation training: build a high-fidelity digital twin environment for offline learning -Security: Design a decision verification mechanism to prevent misjudgment Such research needs to be carried out within the framework of compliance with relevant laws and regulations and national defense and security requirements, focusing on defensive technology research.