AYOLO: Development of a Real-Time Object Detection Model for the Detection of Secretly Cultivated Plants
AYOLO introduces a novel fusion architecture that integrates unsupervised learning techniques with Vision Transformers, leveraging the YOLO series models as its foundation. This innovation enables the effective utilization of rich, unlabeled data, establishing a new pretraining methodology tailored...
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| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/5/2718 |
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| author | Ali Yılmaz Yüksel Yurtay Nilüfer Yurtay |
| author_facet | Ali Yılmaz Yüksel Yurtay Nilüfer Yurtay |
| author_sort | Ali Yılmaz |
| collection | DOAJ |
| description | AYOLO introduces a novel fusion architecture that integrates unsupervised learning techniques with Vision Transformers, leveraging the YOLO series models as its foundation. This innovation enables the effective utilization of rich, unlabeled data, establishing a new pretraining methodology tailored to YOLO architectures. On a custom dataset comprising 80 images of poppy plants, AYOLO achieved a remarkable Average Precision (AP) of 38.7% while maintaining a high rendering speed of 239 FPS (Frames Per Second) on a Tesla K80 GPU. Real-time performance is demonstrated by achieving 239 FPS, and feature fusion optimally combines spatial and semantic information across scales. This performance surpasses the previous state-of-the-art YOLO v6-3.0 by +2.2% AP while retaining comparable speed. AYOLO exemplifies the potential of integrating advanced information fusion techniques with supervised pretraining, significantly enhancing precision and efficiency for object detection models optimized for small, specialized datasets. |
| format | Article |
| id | doaj-art-899d6684f6d344d39c3b71dfe86d867a |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-899d6684f6d344d39c3b71dfe86d867a2025-08-20T02:05:24ZengMDPI AGApplied Sciences2076-34172025-03-01155271810.3390/app15052718AYOLO: Development of a Real-Time Object Detection Model for the Detection of Secretly Cultivated PlantsAli Yılmaz0Yüksel Yurtay1Nilüfer Yurtay2Department of Computer Engineering, Sakarya University, Sakarya 54050, TurkeyDepartment of Computer Engineering, Sakarya University, Sakarya 54050, TurkeyDepartment of Computer Engineering, Sakarya University, Sakarya 54050, TurkeyAYOLO introduces a novel fusion architecture that integrates unsupervised learning techniques with Vision Transformers, leveraging the YOLO series models as its foundation. This innovation enables the effective utilization of rich, unlabeled data, establishing a new pretraining methodology tailored to YOLO architectures. On a custom dataset comprising 80 images of poppy plants, AYOLO achieved a remarkable Average Precision (AP) of 38.7% while maintaining a high rendering speed of 239 FPS (Frames Per Second) on a Tesla K80 GPU. Real-time performance is demonstrated by achieving 239 FPS, and feature fusion optimally combines spatial and semantic information across scales. This performance surpasses the previous state-of-the-art YOLO v6-3.0 by +2.2% AP while retaining comparable speed. AYOLO exemplifies the potential of integrating advanced information fusion techniques with supervised pretraining, significantly enhancing precision and efficiency for object detection models optimized for small, specialized datasets.https://www.mdpi.com/2076-3417/15/5/2718YOLO series algorithmsDETRarchitecturevision transformers (ViT)object detectionFPN (feature pyramid network) |
| spellingShingle | Ali Yılmaz Yüksel Yurtay Nilüfer Yurtay AYOLO: Development of a Real-Time Object Detection Model for the Detection of Secretly Cultivated Plants Applied Sciences YOLO series algorithms DETR architecture vision transformers (ViT) object detection FPN (feature pyramid network) |
| title | AYOLO: Development of a Real-Time Object Detection Model for the Detection of Secretly Cultivated Plants |
| title_full | AYOLO: Development of a Real-Time Object Detection Model for the Detection of Secretly Cultivated Plants |
| title_fullStr | AYOLO: Development of a Real-Time Object Detection Model for the Detection of Secretly Cultivated Plants |
| title_full_unstemmed | AYOLO: Development of a Real-Time Object Detection Model for the Detection of Secretly Cultivated Plants |
| title_short | AYOLO: Development of a Real-Time Object Detection Model for the Detection of Secretly Cultivated Plants |
| title_sort | ayolo development of a real time object detection model for the detection of secretly cultivated plants |
| topic | YOLO series algorithms DETR architecture vision transformers (ViT) object detection FPN (feature pyramid network) |
| url | https://www.mdpi.com/2076-3417/15/5/2718 |
| work_keys_str_mv | AT aliyılmaz ayolodevelopmentofarealtimeobjectdetectionmodelforthedetectionofsecretlycultivatedplants AT yukselyurtay ayolodevelopmentofarealtimeobjectdetectionmodelforthedetectionofsecretlycultivatedplants AT niluferyurtay ayolodevelopmentofarealtimeobjectdetectionmodelforthedetectionofsecretlycultivatedplants |