Abstract: To address the issues of low recognition accuracy for tiny targets and strong background interference in medical glove surface defect detection, this paper proposes a lightweight detection model based on the Transformer architecture, named PAM-DETR. First, the PRT-Block module is constructed based on the RT-DETR model, introducing a re-parameterization structure and an attention mechanism. This significantly enhances the representation capability of small target features while eliminating the computational redundancy of standard convolutions. Second, an Adaptive Sparse Encoding Module (ASEM) is designed to replace the original AIFI structure, thereby optimizing the interaction efficiency across different feature scales. Third, by integrating the ASEM with the newly designed CSPOmniKernel structure, a Multi-Scale Enhanced Feature Pyramid (MSEFP) is constructed to achieve efficient multi-scale fusion of tiny defect features. Finally, a dedicated medical glove defect dataset is built based on industrial on-site collection for validation. Experimental results demonstrate that the improved PAM-DETR algorithm achieves an increase of 4.12 and 4.84 percentage points in the mAP@50 metric compared with the strong baseline models YOLO11n and RT-DETR-R18, respectively. Furthermore, compared with RT-DETR-R18, the number of parameters in PAM-DETR is reduced by 14.5%, and the computational cost (FLOPs) is decreased by 7.9%, effectively meeting the production line’s dual requirements for high-precision and lightweight defect detection of medical gloves.

Key words: medical gloves, defect detection, small object detection, RT-DETR, deep learning