Combining Yolo and Sift to Detect Confusing Objects in Images

Object discovery has advanced significantly with the emergence of deep learning models; however, existing algorithms often fail to deliver highly accurate and feature-focused detection, particularly in challenging visual environments. This study addresses the limitation by proposing a novel framework that integrates the high-level detection capabilities of YOLOv8 with the precision-focused characteristics of SURF-based feature extraction (referred to here as "Surzet"). The proposed method leverages YOLOv8 to perform comprehensive object detection while utilizing Surzet to enhance the identification of fine-grained features and local properties, ensuring robustness to scale and rotation. Experimental evaluation on complex image datasets revealed that this hybrid model significantly outperforms YOLOv8 alone, showing higher detection accuracy and a noticeable reduction in false positives. The initial results demonstrate that integrating YOLOv8 with Surzet creates a more reliable and precise object discovery framework. This approach holds great promise for high-level detection and detailed feature recognition applications.