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This paper introduces a part-aided military vehicle detection model that combines data-driven and knowledge-based approaches to tackle the intricate task of military vehicle detection and classification. Military vehicles, with their distinct and externally recognizable features, serve as an ideal subject for this methodology. Traditional detection systems often struggle with the nuances of military vehicle detection due to the similarities among different models and the complexities introduced by camouflage. Our model transcends these challenges by focusing on the detection of vehicle parts rather than the entire vehicle itself. This part-based detection paradigm not only facilitates the classification of vehicles with minimal training data but also enhances the model’s ability to perform zero-shot detection, where the system classifies vehicles, it has not explicitly been trained on. The model employs open world attributes detection to dynamically adapt to new or modified vehicle. This adaptability is crucial in contemporary conflict scenarios where vehicle modifications are prevalent. Furthermore, the detection of individual parts offers a detailed description of a vehicle’s equipment and functionality, offering a great explanation of the classification process. We observe significant improvements of the part-aided model over a baseline model, particularly in scenarios with sparse training data. This enhancement is attributed to the model’s ability to generalize from part detection to vehicle classification, thereby reducing overfitting risks. The transparency of the classification process is another critical advantage, as it allows users to intuitively understand and verify the classification results based on visible parts. This paper demonstrates the efficacy of the part-aided approach in military vehicle detection. By leveraging features like zero-shot detection and open world attributes, this model paves the way for more robust, adaptable, and self-explainable AI systems in the field of vehicle detection.
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