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Authors: Agniv Chatterjee 1 ; Snehashis Majhi 1 ; Vincent Calcagno 2 and François Brémond 1

Affiliations: 1 INRIA Sophia Antipolis, 2004 Route des Lucioles, 06902, Valbonne, France ; 2 INRAE, Sophia Antipolis FR, Rte des Chappes, 06560, France

Keyword(s): Trich Classification, Trich Detection, Multi-Scale Attention.

Abstract: Trichogramma wasp classification has a significant application in agricultural research, thanks to their massive usage and production in cropping as a bio-control agent. However, classifying these tiny species is a challenging task due to two factors: (i) Detection of these tiny wasps (barely visible with the naked eyes), (ii) Less inter-species discriminative visual features. To combat this, we propose a robust method to detect and classify the wasps from high-resolution images. The proposed method is enabled by a trich detection module that can be plugged into any competitive object detector for improved wasp detection. Further, we propose a multi-scale attention block to encode the inter-species discriminative representation by exploiting the coarse and fine-level morphological structure of the wasps for enhanced wasps classification. The proposed method along with its two key modules is validated in an in-house Trich dataset and a classification performance gain of 4% compared to recently reported baseline approaches outlines the robustness of our method. The code is available at https://github.com/ac5113/TrichANet. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Chatterjee, A.; Majhi, S.; Calcagno, V. and Brémond, F. (2023). TrichANet: An Attentive Network for Trichogramma Classification. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 864-872. DOI: 10.5220/0011677700003417

@conference{visapp23,
author={Agniv Chatterjee. and Snehashis Majhi. and Vincent Calcagno. and Fran\c{C}ois Brémond.},
title={TrichANet: An Attentive Network for Trichogramma Classification},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={864-872},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011677700003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - TrichANet: An Attentive Network for Trichogramma Classification
SN - 978-989-758-634-7
IS - 2184-4321
AU - Chatterjee, A.
AU - Majhi, S.
AU - Calcagno, V.
AU - Brémond, F.
PY - 2023
SP - 864
EP - 872
DO - 10.5220/0011677700003417
PB - SciTePress