Affiliations: [a] College of Artificial Intelligence, North China University of Science and Technology, Tangshan, Hebei, P.R. China
| [b] Key Laboratory of Industrial Intelligent Perception, Tangshan, Hebei, P.R. China
Correspondence:
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Corresponding author. Shujie Li, College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, Hebei, P.R. China. E-mail: lsjncst@163.com.
Abstract: 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.
Keywords: Deep learning, convolutional neural network, lightweight network, military object detection
