实现轻量级军事目标探测

Zhigang Li, Wenhao Nian, Xiaochuan Sun, Shujie Li
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摘要

军事目标 军事目标探测技术是侦察和指挥决策的基础和关键组成部分,在信息化和智能化战争中发挥着重要作用。然而,现有的许多军事目标检测模型都侧重于探索更深层次和更复杂的架构,这就导致模型具有大量参数。这使得它们不适合在移动或资源受限的作战装备上进行推理,如作战头盔和侦察无人机(UAV)。为解决这一问题,本文提出了一种轻量级检测框架。我们的方法提出了一个 CSP-GhostnetV2 模块,使特征提取网络更加轻量级,同时提取更多有效信息。此外,为了在低计算场景下融合多尺度信息,我们使用 GSConv 和所提出的 CSP-RepGhost 组成了一个轻量级特征聚合网络。实验结果表明,与其他检测算法相比,我们提出的轻量级模型在检测精度和效率方面具有显著优势。
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Towards lightweight military object detection
Military object military object detection technology serves as the foundation and critical component for reconnaissance and command decision-making, playing a significant role in information-based and intelligent warfare. However, many existing military object detection models focus on exploring deeper and more complex architectures, which results in models with a large number of parameters. This makes them unsuitable for inference on mobile or resource-constrained combat equipment, such as combat helmets and reconnaissance Unmanned Aerial Vehicles (UAVs). To tackle this problem, this paper proposes a lightweight detection framework. A CSP-GhostnetV2 module is proposed in our method to make the feature extraction network more lightweight while extracting more effective information. Furthermore, to fuse multiscale information in low-computational scenarios, GSConv and the proposed CSP-RepGhost are used to form a lightweight feature aggregation network. The experimental results demonstrate that our proposed lightweight model has significant advantages in detection accuracy and efficiency compared to other detection algorithms.
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