Smart inspection algorithm for data center power facilities based on enhanced YOLOv8 and local attention mechanism

The digital transformation has elevated data centers to critical infrastructure status, essential for the operation of various industries. The stability of power facilities within these centers directly affects their overall service reliability. Traditional manual inspection methods cannot keep up with the growing demands for operation and maintenance due to increasing scale and complexity. This paper introduces an intelligent inspection system for data center power facilities, leveraging an enhanced YOLOv8 algorithm and a Local Attention Mechanism (LAFFE). The proposed system integrates a deeper CSPDarknetX backbone network with a pyramid SPP structure and an optimized detection head featuring keypoint regression and IoU loss. The LAFFE module adaptively enhances salient features and suppresses redundant information, improving fine-grained recognition and real-time performance. Extensive experiments on a large-scale dataset demonstrate the superior accuracy and efficiency of our approach, particularly in detecting small objects amidst complex backgrounds. The system significantly improves inspection efficiency, reduces labor costs, and shows exceptional value in emergency scenarios.