Método de detección del estado de alimentación de los alevines basado en la red ligera YOLO mediante imágenes superficiales poco profundas
Autores: Yang, Haihui; Shi, Yinyan; Wang, Xiaochan
Idioma: Inglés
Editor: MDPI
Año: 2022
Acceso abierto
Artículo científico
2022
Método de detección del estado de alimentación de los alevines basado en la red ligera YOLO mediante imágenes superficiales poco profundas
Categoría
Ingeniería y Tecnología
Subcategoría
Ingeniería Eléctrica y Electrónica
Palabras clave
Alimentación con pellets
Alimentación de alevines
áreas poco profundas bajo el agua
Marco de aprendizaje profundo
Red YOLOv4-Tiny-ECA
Alimentación inteligente
Licencia
CC BY-SA – Atribución – Compartir Igual
Consultas: 24
Citaciones: Sin citaciones
La alimentación de pellets se utiliza ampliamente en la alimentación de alevines, que no pueden hundirse en el fondo en poco tiempo, por lo que la mayoría de los alevines comen en áreas poco profundas bajo el agua. Aiming at the characteristics of fry feeding, we present herein a nondestructive and rapid detection method based on a shallow underwater imaging system and deep learning framework to obtain fry feeding status. Towards this end, images of fry feeding in shallow underwater areas and floating uneaten pellets were captured, following which they were processed to reduce noise and enhance data information. Two characteristics were defined to reflect fry feeding behavior, and a YOLOv4-Tiny-ECA network was used to detect them. The experimental results indicate that the network works well, with a detection speed of 108FPS and a model size of 22.7 MB. Compared with other outstanding detection networks, the YOLOv4-Tiny-ECA network is better, faster, and has stronger robustness in conditions of sunny, cloudy, and bubbles. It indicates that the proposed method can provide technical support for intelligent feeding in factory fry breeding with natural light.
Descripción
La alimentación de pellets se utiliza ampliamente en la alimentación de alevines, que no pueden hundirse en el fondo en poco tiempo, por lo que la mayoría de los alevines comen en áreas poco profundas bajo el agua. Aiming at the characteristics of fry feeding, we present herein a nondestructive and rapid detection method based on a shallow underwater imaging system and deep learning framework to obtain fry feeding status. Towards this end, images of fry feeding in shallow underwater areas and floating uneaten pellets were captured, following which they were processed to reduce noise and enhance data information. Two characteristics were defined to reflect fry feeding behavior, and a YOLOv4-Tiny-ECA network was used to detect them. The experimental results indicate that the network works well, with a detection speed of 108FPS and a model size of 22.7 MB. Compared with other outstanding detection networks, the YOLOv4-Tiny-ECA network is better, faster, and has stronger robustness in conditions of sunny, cloudy, and bubbles. It indicates that the proposed method can provide technical support for intelligent feeding in factory fry breeding with natural light.