A New Exploration: Ancient Book Defect Detection with Attention Mechanisms

With the increasing application of digital technology in cultural heritage preservation, the digitization of ancient books and their defect detection has become an important research topic. Ancient books are prone to a variety of defects, and traditional manual detection methods are inefficient and cannot guarantee accuracy. This paper constructs a specialized dataset containing six types of defects and proposes an improved YOLOv8 network, which is applied to ancient book defect detection for the first time. By introducing three atten-tion mechanisms—CBAM, SEBlock, and ECA—and applying improvements at different positions within the network, the model's ability to recognize de-fects is enhanced. Experimental results show that the improved YOLOv8 mod-el significantly improves detection performance.