Improved Exam Paper Score Detection Algorithm Based on YOLO11n

Abstract

To address the challenges in detecting scores and related information on test papers, such as complex backgrounds and diverse handwriting styles, this paper proposes an improved algorithm based on YOLO11n. A Difference-of-Gaussians downsampling module, DOG-Stem, is designed to enhance edge feature extraction. Moreover, a lightweight grouped detection head, EfficientHead, is constructed, reducing parameters and computational complexity by 10.5% and 15.9%, respectively, while maintaining high performance. Finally, the WIoU loss function is introduced to accelerate model convergence. Experimental results demonstrate that the improved model achieves an mAP50 of 96.3% and an mAP50-95 of 68.6% on the test set, representing increases of 1.3% and 1.6% over the original YOLO11n. The proposed model exhibits superior precision and robustness.