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2019 | OriginalPaper | Chapter

SCOD: Dynamical Spatial Constraints for Object Detection

Authors : Kai-Jun Zhang, Cheng-Hao Guo, Zhong-Han Niu, Lu-Fei Liu, Yu-Bin Yang

Published in: MultiMedia Modeling

Publisher: Springer International Publishing

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Abstract

One-stage detectors are widely used in real-world computer vision applications nowadays due to their competitive accuracy and very fast speed. However, for high resolution (e.g., \(512 \times 512\)) input, most one-stage detectors run too slowly to process such images in real time. In this paper, we propose a novel one-stage detector called Dynamical Spatial Constraints for Object Detection (SCOD). We apply dynamical spatial constraints to address multiple detections of the same object and use two parallel classifiers to address the serious class imbalance. Experimental results show that SCOD makes a significant improvement in speed and achieves competitive accuracy on the challenging PASCAL VOC2007 and PASCAL VOC2012 benchmarks. On VOC2007 test, SCOD runs at 41 FPS with a mAP of 80.4%, which is \(2.2 {\times }\) faster than SSD that runs at 19 FPS with a mAP of 79.8%. On VOC2012 test, SCOD runs at 71 FPS with a mAP of 75.4%, which is \(1.8 {\times }\) faster than YOLOv2 that runs at 40 FPS with a mAP of 73.4%.

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Metadata
Title
SCOD: Dynamical Spatial Constraints for Object Detection
Authors
Kai-Jun Zhang
Cheng-Hao Guo
Zhong-Han Niu
Lu-Fei Liu
Yu-Bin Yang
Copyright Year
2019
Book
MultiMedia Modeling

Print ISBN: 978-3-030-05709-1

Electronic ISBN: 978-3-030-05710-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-05710-7_2