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Swarm Intelligence-Enhanced Detection of Small Objects Using Key Point-Driven YOLO
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Author(s): Shaolong Han (Hebei Baisha Tobacco Co., Ltd., China), Shangrong Wang (Hebei Baisha Tobacco Co., Ltd., China), Wenqi Liu (Hebei Baisha Tobacco Co., Ltd., China), YongQiang Gu (Hebei Baisha Tobacco Co., Ltd., China)and Yujie Zhang (Hebei Baisha Tobacco Co., Ltd., China)
Copyright: 2025
Volume: 16
Issue: 1
Pages: 20
Source title:
International Journal of Swarm Intelligence Research (IJSIR)
Editor(s)-in-Chief: Yuhui Shi (Southern University of Science and Technology (SUSTech), China)
DOI: 10.4018/IJSIR.368649
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Abstract
Traditional object detection methods, such as anchor-based YOLO variants, face challenges due to the irregular shapes and small sizes of these contaminants. This paper introduces a novel approach that leverages swarm Intelligence to enhance the performance of a keypoint-driven YOLO framework. By integrating keypoint detection with Boundary-Aware Vectors (BBAVectors) and utilizing swarm intelligence algorithms for model optimization, our approach improves the localization and identification of small, irregularly shaped non-metallic objects. By optimizing the feature extraction process through swarm-based techniques and incorporating keypoint-driven object detection, our model significantly boosts precision and recall compared to traditional methods. Evaluated on a custom dataset of fiber like materials, our approach achieves a mean Average Precision (mAP) of 92.9% at IoU 0.5, demonstrating strong performance in real-world applications.
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