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计算机系统应用英文版:2020,29(11):139-144
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基于Bi-SSD的小目标检测算法
(华中师范大学 物理科学与技术学院, 武汉 430079)
Small Target Detection Algorithm Based on Bi-SSD
(College of Physical Science and Technology, Central China Normal University, Wuhan 430079, China)
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Received:February 28, 2020    Revised:March 17, 2020
中文摘要: 针对目前目标检测技术中小目标检测困难问题, 提出了一种基于SSD (Single Shot multibox Detector) 改进的小目标检测算法Bi-SSD (Bi-directional Single Shot multibox Detector). 该算法为SSD的浅层特征设计了小目标特征提升模块, 在网络的分类和回归部分结合多尺度特征融合方法和BiFPN (Bi-directional Feature Pyramid Network) 结构, 设计了6尺度BiFPN分类回归子网络. 实验结果表明, 在PASCAL VOC和MS COCO目标检测数据集上Bi-SSD相比原始的SSD算法有更好的检测性能. 其中VOC2007+2012上Bi-SSD算法的mAP指标达到了78.47%相较SSD算法提升了1.34%, 在COCO2017上Bi-SSD算法的mAP达到26.4%提升了接近2.4%.
Abstract:Aiming at the difficulty of small target detection in current target detection technology, a small target detection algorithm named improved Bi-directional Single Shot multibox Detector (Bi-SSD) based on Single Shot multibox Detector (SSD) is proposed. This algorithm designed a small object feature improvement module for the shallow features of SSD. In the classification and regression parts of the network, a 6-scale Bi-directional Feature Pyramid Network (BiFPN) is designed as classification and regression sub-network according to multi-scale feature fusion method and BiFPN structure. Experimental results show that Bi-SSD has better detection performance than the original SSD on PASCAL VOC and MS COCO object detection datasets. On VOC2007+2012, Bi-SSD achieves 78.47% mAP, which is an increase of 1.34% compared to the original SSD algorithm. On COCO2017, Bi-SSD achieves 26.4% mAP, which was an increase of 2.4% compared to the original SSD algorithm.
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汪能,胡君红,刘瑞康,范良辰.基于Bi-SSD的小目标检测算法.计算机系统应用,2020,29(11):139-144
WANG Neng,HU Jun-Hong,LIU Rui-Kang,FAN Liang-Chen.Small Target Detection Algorithm Based on Bi-SSD.COMPUTER SYSTEMS APPLICATIONS,2020,29(11):139-144