Viet Hang Duong
ABSTRACT
Detecting small objects poses a significant challenge in computer vision because of the low resolution and fuzzy feature representation. Although one-stage detection techniques alleviate the problem caused by scale difference to some extent, they also retain redundant features, resulting in resource wastage and slower processing speeds. This research first primarily centers around elucidating the SSD algorithm, a new improved framework, namely Lite FPN-SSD (Lite Single Shot Multibox Detector with Adapting Feature Pyramid Network), then is proposed to solve the weakness of the SSD algorithm. The Lite FPN-SSD is build upon the popular FPN and SSD architectures to create a learnable fusion scheme with controlling the feature information that deep layers deliver to shallow layers. Its lightweight nature, with a minimal increase in parameters, ensures high efficiency for real-time applications. Extensive experiments conducted on VOC, VEDAI and SOHAS datasets demonstrate an impressive results of the proposed models in comparison with the original SSD and its other variations. Particularly, by making a minimal addition of only 2 million parameters, the proposed model achieves a mean average precision (mAP) of 78.36% on the VOC dataset, which is close to another architecture that achieved a 78.40% mAP but require more than 2.6.
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Index Terms
- Lite FPN_SSD: A Reconfiguration SSD with Adapting Feature Pyramid Network Scheme for Small Object Detection
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