Detection of asian hornet’s nest on drone acquired FLIR and color images using deep learning methods
| Autor: | Pascal Desbarats, Tooba Shams |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Ensemble forecasting
Artificial neural network business.industry Computer science Deep learning 02 engineering and technology 010501 environmental sciences 01 natural sciences Object detection Drone Task (project management) Nest 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | IPTA |
| Popis: | Asian hornets are considered a pest because of their dangerousness and their impact on the ecosystem. Detecting nests of this species is a difficult task, as they are found in the trees, hidden in the leaves. Our goal is to carry out this detection from images acquired by a drone. We propose in this work a new method, based on the advantages of visible spectrum and FLIR images. We compare two models of state-of-the-art neural networks (YOLO and Mask-RCNN) for this task. The results are presented from the two separate image sets, then by combining the network responses. To do this, a third dataset (for ensemble model) was built by simulating a FLIR acquisition simultaneous with the acquisition in the visible spectrum. Preliminary results show that the best strategy is to use Mask-RCNN on the ensemble model (detection rate of 93%). A discussion on the relevant information present in the images and on taking into account of this information by the networks is also proposed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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