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.
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