research-article

YOLO-mini-tiger: Amur Tiger Detection

Authors:

Ke LuAuthors Info & Claims

ICMR '20: Proceedings of the 2020 International Conference on Multimedia Retrieval

Pages 517 - 524

https://doi.org/10.1145/3372278.3390710

Published: 08 June 2020 Publication History

Abstract

In this paper, we present our solution for tiger detection in the 2019 Computer Vision for Wildlife Conservation Challenge (CVWC2019). We introduce an efficient deep tiger detector, which consists of the convnet channel adaptation method and an improved tiger detection method based on You Only Look Once version 3 (YOLOv3). Considering the limited memory and computing power of tiny embedded devices, we have used EfficientNet-B0 and Darknet-53 as backbone networks for detection and adapted them to balance their depth and width inspired by the channel pruning method and knowledge distillation method. Our results show that after an architecture adjustment of Darknet-53, the floating-point computation decreases by 93%, its model size decreases by 97%, and its accuracy only decreases by 1%; after an architecture adjustment of EfficientNet-B0, the floating-point computation decreases by 66%, its model size decreases by 70% with its accuracy only decreased by 1%. We also compare GIoU loss and MSE loss in the training stage. The GIoU loss has the advantage that it increases the average AP for IoU from 0.5 to 0.95 without affecting training speed and the interface speed, so it is experimentally reasonable for tiger detection in the wild. This proposed method outperforms previous Amur tiger detection methods presented at CVWC2019.

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Cited By

Al-Shimaysawee LFinn AWeber DSchebella MBrinkworth R(2024)Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial ImagerySensors10.3390/s2421704824:21(7048)Online publication date: 31-Oct-2024
https://doi.org/10.3390/s24217048
Liu BQu Z(2023) AF‐TigerNet: A lightweight anchor‐free network for real‐time Amur tiger ( Panthera tigris altaica ) detection Wildlife Letters10.1002/wll2.120081:1(32-41)Online publication date: 20-Apr-2023
https://doi.org/10.1002/wll2.12008

Index Terms

YOLO-mini-tiger: Amur Tiger Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection

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Published In

cover image ACM Conferences

ICMR '20: Proceedings of the 2020 International Conference on Multimedia Retrieval

June 2020

605 pages

ISBN:9781450370875

DOI:10.1145/3372278

General Chairs:
Cathal Gurrin
Dublin City University, Ireland
,
Björn Þór Jónsson
IT University of Copenhagen, Denmark
,
Noriko Kando
National Institute of Informatics, Tokyo
,
Program Chairs:
Klaus Schoeffmann
Klagenfurt University, Austria
,
Phoebe Chen
La Trobe University, Australia
,
Noel E. O'Connor
Dublin City University, Ireland

Copyright © 2020 ACM.

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 June 2020

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the National Natural Science Foundation of China
the Key Project of the Education Commission of Beijing Municipal
the project of the Education Commission of Beijing Municipal

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ICMR '20

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SIGMM

ICMR '20: International Conference on Multimedia Retrieval

June 8 - 11, 2020

Dublin, Ireland

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Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Al-Shimaysawee LFinn AWeber DSchebella MBrinkworth R(2024)Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial ImagerySensors10.3390/s2421704824:21(7048)Online publication date: 31-Oct-2024
https://doi.org/10.3390/s24217048
Liu BQu Z(2023) AF‐TigerNet: A lightweight anchor‐free network for real‐time Amur tiger ( Panthera tigris altaica ) detection Wildlife Letters10.1002/wll2.120081:1(32-41)Online publication date: 20-Apr-2023
https://doi.org/10.1002/wll2.12008

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