Open AccessADN for object detection
Author(s) -
Wang Jinding,
Hu Haifeng,
Lu Xinlong
Publication year - 2020
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2018.5651
Subject(s) - pyramid (geometry) , computer science , object detection , feature (linguistics) , artificial intelligence , semantic feature , detector , object (grammar) , pattern recognition (psychology) , feature extraction , computer vision , scale (ratio) , data mining , mathematics , cartography , geography , telecommunications , linguistics , philosophy , geometry
Owing to large‐scale diversity and location uncertainty in object detection, how to enrich semantic information has become an important issue that attracts a lot of concern. In this study, the authors propose a novel attentional detection network (ADN) to enrich semantic information of feature maps by adding an extra attention branch to the classic detection network. Compared to previous methods (e.g. feature pyramid network (FPN), single shot multibox detector (SSD)) that producing massive anchors in different layers of feature maps to detect objects with different scales and aspect ratios, which is very time‐consuming, their network is lightweight and do not need to produce extra anchors. Furthermore, ADN can be applied to different object detectors with little computational cost. Extensive experiments indicate that ADN has good detection performance on different datasets without bells and whistles.