Design of an Artificial Vision System to Detect and Control the Presence of Black Vultures at Airfields

Hernando González, Alhm Vera, Diego Valle

Producción científica: Libro / Capitulo del libro / InformeLibros de Investigaciónrevisión exhaustiva

Resumen

Flying bird detection is important to avoid bird-aircraft collisions for aviation safety. It is a challenging task due to the wide variations in the appearance of flying birds. In order to make up for the shortcomings of human eye surveillance, image detection of birds has become an increasingly important issue in digital image processing. According to the experimental observations, detecting and localizing the birds in the image is hard because it can tackle the conditions wherein the birds shown are diverse in shapes and sizes and most importantly the complex backgrounds, they are in. Deep learning-based methods are very robust for this kind of task. The following article presents a comparison of two deep learning methods architectures: Single S hot Detector + MobilenetV2 and Single Shot Detector + InceptionV2 for detection of birds in the air. We used the training and testing dataset provided by COCO dataset. The results show that MobilenetV2 + SSD outperforms InceptionV2 + SSD in processing time and accuracy.

Idioma originalInglés
Título de la publicación alojadaProceedings - International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas589-597
Número de páginas9
ISBN (versión digital)9781665489621
DOI
EstadoPublicada - 2022
Evento2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022 - Trichy, India
Duración: 24 nov. 202226 nov. 2022

Serie de la publicación

NombreProceedings - International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

Conferencia

Conferencia2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022
País/TerritorioIndia
CiudadTrichy
Período24/11/2226/11/22

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