Deep learning technology can be used to enhance the target recognition and attack decision-making capabilities of unmanned ground vehicles. Deep learning is a machine learning method that allows machines to automatically learn and improve the ability of specific tasks from data. In unmanned ground vehicles, deep learning can be applied to the following aspects: 1. **Target recognition**: Using deep learning image recognition technology, various targets such as vehicles, personnel, buildings, etc. can be automatically recognized and classified. 2. **Target tracking**: Using deep learning target tracking algorithms, the position and speed of moving targets can be tracked, thereby improving the vehicle's target recognition and tracking capabilities. 3. **Attack decision**: Using deep learning decision-making algorithms, attack decisions can be made based on factors such as the type, distance, and speed of the identified target, thereby improving the vehicle's attack efficiency and accuracy. In military applications, the use of deep learning technology can enable unmanned ground vehicles to better complete the following tasks: 1. **Reconnaissance**: Using deep learning technology, targets can be automatically recognized and classified, thereby improving reconnaissance efficiency and accuracy. 2. **Target destruction**: Using deep learning technology, targets can be automatically recognized and tracked, thereby improving the accuracy and efficiency of target destruction. 3. **Defense**: Using deep learning technology, enemy targets can be automatically identified and tracked, thereby improving defense capabilities and response speed. However, there are also some challenges and limitations in using deep learning technology, such as: 1. **Data quality**: Deep learning algorithms require large, diverse, and high-quality data to train and verify models. 2. **Computing resources**: Deep learning algorithms require a large amount of computing resources to run and train models. 3. **Security**: Deep learning algorithms may have security issues, such as being hacked or data leaked. In summary, the use of deep learning technology can enhance the target recognition and attack decision-making capabilities of unmanned ground vehicles, but issues such as data quality, computing resources, and security need to be addressed.