# Application of deep learning in target recognition and decision-making of unmanned ground vehicles Deep learning technology can indeed significantly enhance the target recognition and decision-making capabilities of unmanned ground vehicles (UGVs). The following are some feasible application directions: ## Enhanced target recognition ability -**Multi-modal perception fusion**: Combining multi-sensor data such as vision, radar, infrared, etc. -Target detection in complex environments**: Convolutional neural network (CNN) is used to achieve stable recognition under conditions of high occlusion and low light. -**Target classification and precise positioning**: Use algorithms such as YOLO and Faster R-CNN to achieve real-time target detection and classification ## Enhanced decision-making ability -**Scenario understanding and situational awareness**: Deep learning can help the system understand the overall environment and determine the threat level of each target -**Enhanced learning application**: Through simulation training, the system learns to optimize decision-making strategies -**Intelligent decision support**: Provide auxiliary decision-making recommendations based on task requirements and environmental conditions The application of these technologies should be developed and deployed under the premise of ensuring system reliability, safety and ethical principles, and always maintain human ultimate control over key decision-making.