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OPTIMIZING YOLOV8N FOR ENHANCED PRECISION IN SMALL OBJECT DETECTION ON CUSTOM DATASETS
Issue Vol. 1 No. 01 (2024): Volume 01 Issue 01 --- Section Articles
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Abstract
Object detection, a fundamental task in computer vision, has witnessed significant advancements with the advent of deep learning. While state-of-the-art models like the YOLO series exhibit impressive performance across various applications, the accurate detection of small objects remains a persistent challenge. This article presents a comprehensive study on enhancing the YOLOv8n architecture, the smallest variant of the YOLOv8 family, specifically for improved small object recognition within custom datasets. We explore architectural modifications, advanced loss functions, and refined training strategies to bolster its capabilities. Experimental results on a simulated custom dataset, representative of scenarios with prevalent small targets, demonstrate that our refined YOLOv8n achieves superior performance metrics compared to its baseline counterpart, particularly in mean Average Precision (mAP) for small objects. These findings underscore the potential of targeted enhancements to off-the-shelf models for specialized object detection tasks.
Keywords
References
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