Small object detection in aerial images using convolutional neural networks

Bazhenov S.A., Khodyrev R.R., Kabanova T.V., Shipilov S.E., Vrazhnov D.A., Kistenev Y.V.
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This paper addresses the problem of detecting small objects in visible-spectrum aerial imagery. High background variability and weak feature saliency make small object detection a non-trivial task. Under such conditions, classical computer vision algorithms based on hand-crafted descriptors exhibit low efficiency, prompting a shift towards deep neural network architectures, which demonstrate superior generalization capability and robustness to false positives. We selected a one-stage approach based on a neural network predictive model as the primary object detection method. Also, several neural network architectures belonging to this class were analyzed, outlining their advantages and disadvantages. As the baseline detector, we adopted YOLO version 11 and incorporated a multi-scale feature aggregation module (which combines information from neural network layers operating at different scales) and a dimension-aware selective integration module (which automatically determines the dimension (channel, height, or width) along which to process features and fuses them selectively). These modifications aim to both enhance computational efficiency, enabling deployment of neural network models onboard aerial vehicles for real-time image analysis, and improve small object detection accuracy. Given the complexity of image annotation, we used an open synthetic image database containing approximately 4 000 images for training and testing (with a 90/10 split, respectively). We extend the training set using various random augmentation techniques. To evaluate the performance of the resulting predictive models, we employed mean Average Precision across all classes, using both a fixed 50% intersectionover- union threshold and a varying threshold from 50% to 95%. Overfitting was monitored by analyzing loss curves during training process. The proposed modifications to the YOLO architecture reduced image processing time by a factor of two while maintaining detection accuracy.

Keywords: small object detection, computer vision, convolutional neural networks, aerial imagery
Citation in English: Bazhenov S.A., Khodyrev R.R., Kabanova T.V., Shipilov S.E., Vrazhnov D.A., Kistenev Y.V. Small object detection in aerial images using convolutional neural networks // Computer Research and Modeling, 2026, vol. 18, no. 4, pp. 855-870
Citation in English: Bazhenov S.A., Khodyrev R.R., Kabanova T.V., Shipilov S.E., Vrazhnov D.A., Kistenev Y.V. Small object detection in aerial images using convolutional neural networks // Computer Research and Modeling, 2026, vol. 18, no. 4, pp. 855-870
DOI: 10.20537/2076-7633-2026-18-4-855-870
URL: http://crm-en.ics.org.ru/journal/article/3749/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

Copyright © 2026 Bazhenov S.A., Khodyrev R.R., Kabanova T.V., Shipilov S.E., Vrazhnov D.A., Kistenev Y.V.

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