Модель та метод навчання детектора об’єктів на аерозображенні з оптимізацією робастності та кількості обчислень

• Published in: Avìacìjno-kosmìčna tehnìka ì tehnologìâ no. 5
• Main Authors: Moskalenko, Alona, Zaretskyi, Mykola, Vynohradov, Maksym, Babych, Vladyslav
• Format: Journal Article
• Language: English
• Published: 25.10.2024
• ISSN: 1727-7337; 2663-2217; 2663-2217
• DOI: 10.32620/aktt.2024.5.11

The subject of research is Neural network-based object detectors, which are widely used for video image analysis. An increasing number of tasks now demand data processing directly at the source, which limits the available computational resources. However, the vulnerability of neural networks to noise, adversarial attacks, and weight error injections significantly diminishes their robustness and overall effectiveness. The relevant task is to develop models that provide both computational efficiency and robustness against perturbations. This paper investigates a model and method for enhancing the robustness of neural network detectors under limited resources. The objective is to design a model that allocates resources optimally while maintaining stability. To achieve this, the study employs techniques such as dynamic neural networks, robustness optimization, and resilience strategies. The following results were obtained. A detector with a feature extractor based on ViT-S/16, modified with gate modules for dynamic examination was developed. The model was trained on the RSOD dataset and meta-learned on the adaptation results to various perturbations. The model's resistance to random bit inversions in weights (10 % of weights) and to adversarial attacks with perturbation amplitudes up to 3/255 (L∞ norm) was tested. Conclusion. The proposed detector model incorporating dynamic examination and optimized robustness, reduced floating-point operations by over 20 % without loss of accuracy. A novel method for training the detector was developed, combining the RetinaNet loss function with the loss function of gate blocks and applying meta-learning on the adaptation results for various types of synthetic perturbations. Testing demonstrated an increase in accuracy by 11.9 % under the influence of error injection and by 13.2 % under the influence of adversarial attacks.