Pointer-Type Dashboard Recognition Algorithm for Real-Time Detection——An Improved Method Based on YOLOv8

With the development of intelligent and automated technologies, pointer-type instruments are still widely used in various fields. To improve the recognition accuracy and real-time performance of pointer-type instruments, this paper proposes an improved algorithm based on YOLOv8. The algorithm integrates the DetectAFPN detection head and CGAttention attention mechanism into the original YOLOv8 architecture for optimization. By introducing the DetectAFPN detection head, the algorithm can more effectively handle features at different scales, while the CGAttention attention mechanism enhances the network's focus on important features, thus improving recognition accuracy. Additionally, this paper performs lightweight optimization on the original model, reducing computational resource consumption and improving real-time detection capability. Experimental results show that the improved model outperforms the traditional YOLOv8 in several metrics, achieving more accurate and efficient pointer-type instrument recognition.