The use of deep learning technology can significantly enhance the target recognition and attack decision-making capabilities of unmanned ground vehicles. The following is a detailed introduction from multiple aspects: - **Target recognition capability** - **Powerful feature extraction**: The convolutional neural network (CNN) in deep learning has excellent feature extraction capabilities. In the application scenario of unmanned ground vehicles, it can automatically learn various features of the target, such as shape, texture, color, etc. from a large amount of image or video data. For example, for enemy vehicles on the battlefield, CNN can accurately capture the unique features such as the vehicle's outline, turret shape, and body logo, thereby achieving accurate recognition. - **Handling complex scenarios**: The environment faced by unmanned ground vehicles is often complex and changeable, and deep learning technology can effectively cope with this challenge. By training on large-scale and diverse data sets, the model can learn the appearance changes of targets in different scenarios. For example, in various terrain and background conditions such as urban streets, mountains, and deserts, deep learning models can accurately identify targets without misjudgment due to environmental interference. - **Multi-sensor fusion**: Unmanned ground vehicles are usually equipped with multiple sensors, such as cameras, lidar, millimeter-wave radar, etc. Deep learning technology can fuse these multi-source sensor data to further improve the accuracy and reliability of target recognition. For example, combining visual image information with LiDAR point cloud data and using deep learning algorithms for joint analysis can provide a more comprehensive understanding of the characteristics and spatial position of the target, greatly improving recognition accuracy. - **Attack decision-making capability** - **Situational awareness and understanding**: Deep learning models can fully perceive and understand the battlefield situation. By analyzing sensor data, it can identify the relationship between multiple targets, the interaction between targets and the surrounding environment, and the potential threat level. For example, judging the formation form and driving direction of multiple enemy vehicles, as well as the threat level they may pose to our unmanned ground vehicles, provides a rich information basis for attack decisions. - **Intelligent decision-making algorithm**: Reinforcement learning algorithms based on deep learning can enable unmanned ground vehicles to learn the optimal attack decision-making strategy in complex environments. In the process of continuous interaction with the environment, the vehicle adjusts its behavior strategy based on the reward feedback obtained (such as positive rewards for successfully hitting the target, and negative rewards for being attacked by the enemy or missing the target). For example, learn when to launch an attack, what attack weapons and methods to choose, in order to achieve maximum combat effectiveness and ensure its own safety. - **Real-time decision support**: Deep learning models have the ability to process data quickly, analyze target recognition results and battlefield situations in a short period of time, and give real-time attack decision suggestions. This is crucial for unmanned ground vehicles to respond in a timely manner on a rapidly changing battlefield. For example, when a new enemy target is suddenly found to enter the attack range, the deep learning system can quickly assess the situation and develop a reasonable attack plan in a short period of time to ensure that the vehicle can respond to the threat in a timely and effective manner.