Abstract
Small object detection is a crucial task in computer vision due to its wide range of real-world applications. Detecting small objects accurately and efficiently remains a challenging task due to the reduced size of the objects, low contrast to their surroundings, and potential occlusions. To tackle this issue, we proposed a method for detecting small objects in object detection tasks, including a new strategy for balancing positive and negative samples, a loss function that adapts the weight of detection losses according to object size, and an anchor mechanism that accommodates objects with diverse sizes and aspect ratios. The experimental data substantiates that our method has achieved a 12.9% increase in average accuracy for small objects on the COCO dataset, compared to the baseline.
S. Zhang and Y. Sun—Contributed equally to this work.
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Acknowledgement
This work was supported by “Youth Innovative Research Team of Capital Normal University”, Project of High-level Teachers in Beijing Municipal Universities in the Period of 13th Five-year Plan CIT &TCD201804075 and STCSM 18DZ2270700.
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Zhang, S., Sun, Y., Su, J., Gan, G., Wen, Z. (2023). Adaptive Training Strategies for Small Object Detection Using Anchor-Based Detectors. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14260. Springer, Cham. https://doi.org/10.1007/978-3-031-44195-0_3
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