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YOLO Algorithm-Based Effective Orange Detection and Localization with Improved Data Augmentation
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DOI：10.1007/978-981-96-7807-5_32.png)
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Abstract
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This paper presents a robust framework for You Only Look Once (YOLO) algorithm-based orange detection and localization in photos and videos is presented. The system combines contour-based bounding box localization with deep learning-based item recognition for increased accuracy. Transfer learning was used to refine a pre-trained YOLOv10 model on a Fruit 360 dataset. Data augmentation techniques such as random rotations, brightness changes, and scaling were applied to improve the model's resilience. Bounding boxes are created around identified oranges with a confidence threshold greater than 0.5 as part of the real-time video processing methodology. The model performed well on a balanced test dataset, achieving 95% accuracy, 92% precision, and 90% recall. These findings show how well YOLO works when combined with conventional computer vision methods for real-world uses like automated fruit sorting, fruit harvesting, and real-time market monitoring. The processed video output confirms the system's suitability for real-world situations.
Keywords:
YOLO
Object detection
Orange detection
Fruit localization
Data augmentation
Transfer learning
Contour-based bounding boxes
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