A Classwise Vulnerable Part Detection Method for Military Targets

Abstract

Accurate vulnerable part detection based on full target detection results shows great importance in improving the damage effectiveness of the military drone. However, traditional object detection methods have difficulty in handling inaccurate full target bounding boxes and fail to model the semantic relationships between various class full targets and their key parts, resulting in low localization accuracy. The proposed approach includes a classwise feature recalibration module, which effectively models the dependencies between the prior knowledge obtained from the full target detector and the location of the key part. Additionally, an optimized spatial transformation module is designed to preprocess the input image and eliminate interfering objects. Furthermore, a carefully constructed loss function is employed, linking the classification branch with the regression branch, thereby emphasizing the importance of localization accuracy. Our proposed model surpasses the performance of existing state-of-the-art models, demonstrating a significant advantage with maximum improvements of +24.9%, +30.2%, and +28.3% in mean average precision on the standard test set, generalized test set, and real-world dataset, respectively. The effectiveness and robustness are also confirmed through extensive ablation studies.

Keywords

• Deep learning
• key parts
• military targets
• prior knowledge