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Authors: Angelos-Michael Papadopoulos ; Paschalis Melissas ; Anestis Kastellos ; Panagiotis Katranitsiotis ; Panagiotis Zaparas ; Konstantinos Stavridis and Petros Daras

Affiliation: The Visual Computing Lab - Centre for Research and Technology Hellas/Information Technologies Institute, Thessaloniki, Greece

Keyword(s): TenebrioVision, Dataset, Benchmark, Instance Segmentation, Object Detection, Worms, Tenebrio Molitor, Edible Insects, Farming, Automation, Alternative Food Source.

Abstract: Tenebrio molitor worms have shown extreme nutritional benefits, as they contain useful natural compounds, making them worth as an alternative food source. It is beneficial for insect farms to have automated mechanisms that can detect these worms. Without an explicitly annotated dataset, the task of detecting tenebrio molitor worms remains challenging and underdeveloped. To address this issue, we introduce TenebrioVi-sion, which is a fully annotated dataset, suitable for the detection and segmentation of tenebrio molitor larvae worms. The data acquisition is performed in a controlled environment. The dataset consists of 1,120 images, with a total of 53,600 worm instances. The 1,120 images are equally distributed on 14 distinct levels, each level containing a specific number of tenebrio monitor larvae worms. The dataset is validated in terms of mean average precision, memory allocation, and inference time, on several state-of-the-art baseline methods for both detection and segmentation purposes. The results unequivocally show that the detection and segmentation accuracy is high on both TenebrioVision and real farm images. (More)

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Paper citation in several formats:
Papadopoulos, A.; Melissas, P.; Kastellos, A.; Katranitsiotis, P.; Zaparas, P.; Stavridis, K. and Daras, P. (2024). TenebrioVision: A Fully Annotated Dataset of Tenebrio Molitor Larvae Worms in a Controlled Environment for Accurate Small Object Detection and Segmentation. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-684-2; ISSN 2184-4313, SciTePress, pages 187-196. DOI: 10.5220/0012295900003654

@conference{icpram24,
author={Angelos{-}Michael Papadopoulos. and Paschalis Melissas. and Anestis Kastellos. and Panagiotis Katranitsiotis. and Panagiotis Zaparas. and Konstantinos Stavridis. and Petros Daras.},
title={TenebrioVision: A Fully Annotated Dataset of Tenebrio Molitor Larvae Worms in a Controlled Environment for Accurate Small Object Detection and Segmentation},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2024},
pages={187-196},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012295900003654},
isbn={978-989-758-684-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - TenebrioVision: A Fully Annotated Dataset of Tenebrio Molitor Larvae Worms in a Controlled Environment for Accurate Small Object Detection and Segmentation
SN - 978-989-758-684-2
IS - 2184-4313
AU - Papadopoulos, A.
AU - Melissas, P.
AU - Kastellos, A.
AU - Katranitsiotis, P.
AU - Zaparas, P.
AU - Stavridis, K.
AU - Daras, P.
PY - 2024
SP - 187
EP - 196
DO - 10.5220/0012295900003654
PB - SciTePress