Yes, deep learning technology can effectively enhance the target recognition and attack decision-making capabilities of unmanned ground vehicles (UGVs). Here are some specific ways: 1. **Target detection and recognition**: Using convolutional neural networks (CNNs) can improve the accuracy and efficiency of target recognition. Models such as YOLO (You Only Look Once) and Faster R-CNN perform well in real-time target detection, which can help UGVs quickly identify and classify objects in the field of view. 2. **Semantic segmentation**: Through semantic segmentation of images using deep learning technology, UGVs can obtain a more detailed description of the surrounding environment, which is crucial for navigation and target selection in complex environments. 3. **Path planning and decision-making**: Through deep reinforcement learning (DRL), UGVs can learn optimal path planning and decision-making in dynamic environments. This helps the vehicle automatically adapt to environmental changes and potential threats when performing tasks. 4. **Target tracking**: Recurrent neural networks (RNNs) or long short-term memory networks (LSTM) in deep learning can be used to track moving targets, helping UGVs maintain continuous awareness of targets during attack decisions. 5. **Sensor fusion**: Through deep learning algorithms, various sensor data (such as vision, infrared, lidar, etc.) are integrated to improve the overall perception of the environment by UGV. 6. **Attack decision optimization**: Use deep learning models to analyze historical data and real-time intelligence, optimize attack strategies, and improve the success rate of mission execution. Deep learning technology can significantly improve the performance of unmanned ground vehicles in military and industrial applications by improving recognition accuracy, enhancing environmental perception capabilities, and optimizing decision-making. However, in the actual deployment process, factors such as computing resources, processing speed, and system reliability and security must also be considered.