Enhancing Small Object Detection with YOLO: A Novel Framework for Improved Accuracy and Efficiency Article Swipe

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Moghadami, Mahila , Keyvanrad, Mohammad Ali , Sabaghian, Melika · YOU? · · 2025 · Open Access ·

This paper investigates and develops methods for detecting small objects in large-scale aerial images. Current approaches for detecting small objects in aerial images often involve image cropping and modifications to detector network architectures. Techniques such as sliding window cropping and architectural enhancements, including higher-resolution feature maps and attention mechanisms, are commonly employed. Given the growing importance of aerial imagery in various critical and industrial applications, the need for robust frameworks for small object detection becomes imperative. To address this need, we adopted the base SW-YOLO approach to enhance speed and accuracy in small object detection by refining cropping dimensions and overlap in sliding window usage and subsequently enhanced it through architectural modifications. we propose a novel model by modifying the base model architecture, including advanced feature extraction modules in the neck for feature map enhancement, integrating CBAM in the backbone to preserve spatial and channel information, and introducing a new head to boost small object detection accuracy. Finally, we compared our method with SAHI, one of the most powerful frameworks for processing large-scale images, and CZDet, which is also based on image cropping, achieving significant improvements in accuracy. The proposed model achieves significant accuracy gains on the VisDrone2019 dataset, outperforming baseline YOLOv5L detection by a substantial margin. Specifically, the final proposed model elevates the mAP .5.5 accuracy on the VisDrone2019 dataset from the base accuracy of 35.5 achieved by the YOLOv5L detector to 61.2. Notably, the accuracy of CZDet, which is another classic method applied to this dataset, is 58.36. This research demonstrates a significant improvement, achieving an increase in accuracy from 35.5 to 61.2.

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article

Landing Page

http://arxiv.org/abs/2512.07379

PDF

https://arxiv.org/pdf/2512.07379

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green

OpenAlex ID

https://openalex.org/W7113915223

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OpenAlex ID

https://openalex.org/W7113915223

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Title

Enhancing Small Object Detection with YOLO: A Novel Framework for Improved Accuracy and Efficiency

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Type

article

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Publication year

2025

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Publication date

2025-12-08

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Authors

Moghadami, Mahila, Keyvanrad, Mohammad Ali, Sabaghian, Melika

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https://arxiv.org/abs/2512.07379

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https://arxiv.org/pdf/2512.07379

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Open access

Yes

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OA status

green

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https://arxiv.org/pdf/2512.07379

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Concepts

Object detection, Artificial intelligence, Computer science, Aerial image, Feature (linguistics), Computer vision, Sliding window protocol, Feature extraction, Object (grammar), Detector, Window (computing), Image (mathematics), Pattern recognition (psychology), Channel (broadcasting), Change detection, Base (topology), Cropping, Feature detection (computer vision), Row, Baseline (sea), Image processing, Data mining, Aerial imagery

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