Integration of YOLOv8 Small and MobileNet V3 Large for Efficient Bird Detection and Classification on Mobile Devices
Background: Bird species identification and classification are crucial for biodiversity research, conservation initiatives, and ecological monitoring. However, conventional identification techniques used by biologists are time-consuming and susceptible to human error. The integration of deep learnin...
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MDPI AG
2025-03-01
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| author | Axel Frederick Félix-Jiménez Vania Stephany Sánchez-Lee Héctor Alejandro Acuña-Cid Isaul Ibarra-Belmonte Efraín Arredondo-Morales Eduardo Ahumada-Tello |
| author_facet | Axel Frederick Félix-Jiménez Vania Stephany Sánchez-Lee Héctor Alejandro Acuña-Cid Isaul Ibarra-Belmonte Efraín Arredondo-Morales Eduardo Ahumada-Tello |
| author_sort | Axel Frederick Félix-Jiménez |
| collection | DOAJ |
| description | Background: Bird species identification and classification are crucial for biodiversity research, conservation initiatives, and ecological monitoring. However, conventional identification techniques used by biologists are time-consuming and susceptible to human error. The integration of deep learning models offers a promising alternative to automate and enhance species recognition processes. Methods: This study explores the use of deep learning for bird species identification in the city of Zacatecas. Specifically, we implement YOLOv8 Small for real-time detection and MobileNet V3 for classification. The models were trained and tested on a dataset comprising five bird species: Vermilion Flycatcher, Pine Flycatcher, Mexican Chickadee, Arizona Woodpecker, and Striped Sparrow. The evaluation metrics included precision, recall, and computational efficiency. Results: The findings demonstrate that both models achieve high accuracy in species identification. YOLOv8 Small excels in real-time detection, making it suitable for dynamic monitoring scenarios, while MobileNet V3 provides a lightweight yet efficient classification solution. These results highlight the potential of artificial intelligence to enhance ornithological research by improving monitoring accuracy and reducing manual identification efforts. |
| format | Article |
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| institution | DOAJ |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | AI |
| spelling | doaj-art-eb9761a2aa5343609d58cdd323c0433e2025-08-20T02:41:51ZengMDPI AGAI2673-26882025-03-01635710.3390/ai6030057Integration of YOLOv8 Small and MobileNet V3 Large for Efficient Bird Detection and Classification on Mobile DevicesAxel Frederick Félix-Jiménez0Vania Stephany Sánchez-Lee1Héctor Alejandro Acuña-Cid2Isaul Ibarra-Belmonte3Efraín Arredondo-Morales4Eduardo Ahumada-Tello5Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria de Ingeniería Campus Zacatecas (UPIIZ), Academia de Ciencias de la Computación, Zacatecas 98160, MexicoInstituto Politécnico Nacional, Unidad Profesional Interdisciplinaria de Ingeniería Campus Zacatecas (UPIIZ), Academia de Ciencias de la Computación, Zacatecas 98160, MexicoInstituto Politécnico Nacional, Unidad Profesional Interdisciplinaria de Ingeniería Campus Zacatecas (UPIIZ), Academia de Ciencias de la Computación, Zacatecas 98160, MexicoCentro de Investigación en Matemáticas (CIMAT), Unidad Zacatecas, Departamento de Ingeniería de Software, Zacatecas 98160, MexicoInstituto Politécnico Nacional, Unidad Profesional Interdisciplinaria de Ingeniería Campus Zacatecas (UPIIZ), Academia de Ciencias de la Computación, Zacatecas 98160, MexicoFacultad de Contaduría y Administración, Universidad Autónoma de Baja California, Tijuana 22424, MexicoBackground: Bird species identification and classification are crucial for biodiversity research, conservation initiatives, and ecological monitoring. However, conventional identification techniques used by biologists are time-consuming and susceptible to human error. The integration of deep learning models offers a promising alternative to automate and enhance species recognition processes. Methods: This study explores the use of deep learning for bird species identification in the city of Zacatecas. Specifically, we implement YOLOv8 Small for real-time detection and MobileNet V3 for classification. The models were trained and tested on a dataset comprising five bird species: Vermilion Flycatcher, Pine Flycatcher, Mexican Chickadee, Arizona Woodpecker, and Striped Sparrow. The evaluation metrics included precision, recall, and computational efficiency. Results: The findings demonstrate that both models achieve high accuracy in species identification. YOLOv8 Small excels in real-time detection, making it suitable for dynamic monitoring scenarios, while MobileNet V3 provides a lightweight yet efficient classification solution. These results highlight the potential of artificial intelligence to enhance ornithological research by improving monitoring accuracy and reducing manual identification efforts.https://www.mdpi.com/2673-2688/6/3/57YOLOv8 SmallMobileNet V3TensorFlowLitebird classificationautomated samplingmobile app |
| spellingShingle | Axel Frederick Félix-Jiménez Vania Stephany Sánchez-Lee Héctor Alejandro Acuña-Cid Isaul Ibarra-Belmonte Efraín Arredondo-Morales Eduardo Ahumada-Tello Integration of YOLOv8 Small and MobileNet V3 Large for Efficient Bird Detection and Classification on Mobile Devices AI YOLOv8 Small MobileNet V3 TensorFlowLite bird classification automated sampling mobile app |
| title | Integration of YOLOv8 Small and MobileNet V3 Large for Efficient Bird Detection and Classification on Mobile Devices |
| title_full | Integration of YOLOv8 Small and MobileNet V3 Large for Efficient Bird Detection and Classification on Mobile Devices |
| title_fullStr | Integration of YOLOv8 Small and MobileNet V3 Large for Efficient Bird Detection and Classification on Mobile Devices |
| title_full_unstemmed | Integration of YOLOv8 Small and MobileNet V3 Large for Efficient Bird Detection and Classification on Mobile Devices |
| title_short | Integration of YOLOv8 Small and MobileNet V3 Large for Efficient Bird Detection and Classification on Mobile Devices |
| title_sort | integration of yolov8 small and mobilenet v3 large for efficient bird detection and classification on mobile devices |
| topic | YOLOv8 Small MobileNet V3 TensorFlowLite bird classification automated sampling mobile app |
| url | https://www.mdpi.com/2673-2688/6/3/57 |
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