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.
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