Application of computer vision technology in interactive teaching tools

As the digital age progresses, the intersection of computer vision technology and education is drawing significant attention, enabling the development of interactive teaching tools that redefine how knowledge is imparted. While these tools promise to enhance the educational experience through increased engagement and personalized learning pathways, a critical enabling component is the ability to swiftly and reliably detect and interpret educational content and student interactions. This paper puts forth an improved object detection paradigm within this context using an advanced YOLO (You Only Look Once) algorithm modified for the unique requirements of interactive educational environments. We introduce a secondary feature fusion technique within the YOLO framework to enhance detection accuracy—key to maintaining the flow and interactivity of the educational experience. The innovative approach includes lightweight separable convolutions replacing traditional convolution modules to minimize network load and an enrichment of the DarkNet-53 backbone with dense connections for better feature propagation. In addition, we implement a dual-stage feature fusion strategy that consolidates the strengths of different network layers to ensure comprehensive feature extraction. Our proposed enhanced YOLO algorithm addresses the persisting challenge of precision in object detection to effectively facilitate the deployment of computer vision in interactive teaching, propelling the education sector towards a more engaging and sophisticated future.