Poly-YOLO: higher speed, more precise detection and instance segmentation for YOLOv3

We present a new version of YOLO with better performance and extended with instance segmentation called Poly-YOLO. Poly-YOLO builds on the original ideas of YOLOv3 and removes two of its weaknesses: a large amount of rewritten labels and inefficient distribution of anchors. Poly-YOLO reduces the iss...

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Bibliographic Details
Published inarXiv.org
Main Authors Hurtik, Petr, Molek, Vojtech, Hula, Jan, Vajgl, Marek, Vlasanek, Pavel, Nejezchleba, Tomas
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 29.05.2020
Subjects
Apexes
Electronic devices
Embedded systems
Image segmentation
Instance segmentation
Parameters
Polygons
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Summary:We present a new version of YOLO with better performance and extended with instance segmentation called Poly-YOLO. Poly-YOLO builds on the original ideas of YOLOv3 and removes two of its weaknesses: a large amount of rewritten labels and inefficient distribution of anchors. Poly-YOLO reduces the issues by aggregating features from a light SE-Darknet-53 backbone with a hypercolumn technique, using stairstep upsampling, and produces a single scale output with high resolution. In comparison with YOLOv3, Poly-YOLO has only 60% of its trainable parameters but improves mAP by a relative 40%. We also present Poly-YOLO lite with fewer parameters and a lower output resolution. It has the same precision as YOLOv3, but it is three times smaller and twice as fast, thus suitable for embedded devices. Finally, Poly-YOLO performs instance segmentation using bounding polygons. The network is trained to detect size-independent polygons defined on a polar grid. Vertices of each polygon are being predicted with their confidence, and therefore Poly-YOLO produces polygons with a varying number of vertices.
ISSN:2331-8422