Open AccessObject detection based on Yolov4-Tiny and Improved Bidirectional feature pyramid network
Author(s) -
Qi Liu,
Xiaoyu Fan,
Zhipeng Xi,
Zhijian Yin,
Zhen Yang
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2209/1/012023
Subject(s) - computer science , pascal (unit) , object detection , merge (version control) , pooling , artificial intelligence , pattern recognition (psychology) , feature extraction , data mining , feature (linguistics) , linguistics , philosophy , information retrieval , programming language
In the field of small object detection, Yolov4-Tiny is inadequate in feature extraction and does not make best of multi-scale features. In this paper, an improved BiFPN framework is proposed based on Yolov4-Tiny to increase object detection precision. Moreover, the Yolov4-Tiny is taken as the backbone network and introduce spatial pyramid pooling (SPP) to connect and merge multi-scale regions. Finally, our method can achieve 79.53% map on Pascal VOC dataset, which is 2.12% higher than the original Yolov4-Tiny model.