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Submunition swarm combat is a trend in modern battlefields. It aims to achieve precise and organized destruction of time-sensitive and mobile target groups in large operational depth, especially in GNSS-denied environments. This approach relies on assessing the target identification, positioning, and real-time damage assessment of submunition. After collecting damaged images, it is necessary to carry out damage information detection, including explosion flames, smoke, and other information, to determine the impact point of submunition and the process of damaging the target. However, when applying these methods to evaluate submunition, the performance of convolutional neural networks to extract target features still needs further improvement. This paper addresses the problem of false changes in images caused by projectile disturbances in complex backgrounds and the low accuracy of damage feature detection. Building upon the CosNet attention neural network, this paper uses an attention mechanism and proposes a damage feature extraction method based on spatiotemporal attention neural networks. This method achieves high-precision semantic segmentation of damage regions in continuous video sequences, providing a foundation for determining the impact point of submunition and assessing the damage effect. Through our simulation and experiment carried out by rocket sleds, the evaluation of submunition in orbital regions achieved realtime target identification and real-time extraction of the flare region, which validated the effectiveness of the spatiotemporal attention neural network in extracting damage regions in actual dynamic environments. This research provides a critical foundation for damage assessment, offering solutions that enhance the accuracy and reliability of realtime change detection in damage regions within high-dynamic environments.
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