## 5 Conclusions  
Despite its remarkable achievements in defect monitoring, deep learning technology cannot successfully be fully utilized for defect detection in nitrile medical gloves. Therefore, herein, a CCA-YOLO algorithm for glove defect detection is proposed. A detection layer for small targets was added to improve the detection accuracy for glove scratches. CCA mechanisms were introduced to obtain a larger receptive field and further improve the feature extraction ability of the network for tear and scratch defects. Furthermore, to improve the positioning accuracy and reduce the target omission rate, the EIoU was introduced to detect glove tear defects and \(\alpha\)-IoU was introduced to detect glove scratch defects. With the  
Figure 9: Comparison of the monitoring effect. (**a**) YOLOv5 detection results. (**b**) CCA-YOLO detection results.  
experimental equipment unchanged, the mAP values of the three defect detection models for the detection of horizontal tears, vertical tears, and scratches reached 99.3%, 99.8%, and 99.6%, respectively, exhibiting increments of 4.2, 5.3, and 12.4%, respectively, compared with those of the original YOLOv5s. On the basis of the considerably improved mAP, the frame rate reached 36.8 FPS in the experimental environment, meeting the requirements of the real-time detection of glove defects. CCA-YOLO is very applicable to small-target detection and can solve most of the defect detection problems, and thus, can be extended to the industrial surface defect detection field.  
Our experiments show that the proposed algorithm exhibits a high accuracy and recall rate as well as a small model size; furthermore, the algorithm can be easily deployed and meets the requirements for detecting glove tears, scratches, and other defects. However, there are a few limitations, mainly in the following two aspects:  
1. The performance of the model reaches a bottleneck using the current hardware; however, in the actual application environment, the hardware requirements are higher and there are more restrictions on the physical size or use environment. The complex network structure increases the number of parameters and calculation complexity, which is not conducive to the deployment of the model in practice.
2. In practical applications, the defects of nitrile medical gloves are not limited to tears and scratches. There are many types of defects with different characteristics; therefore, the model needs to be further improved to detect more types of defects. Future studies will aim to decrease the number of model parameters, increase the types of detected defects, further reduce the consumption of labor costs, and accelerate the development of the intelligent detection of defects in nitrile medical gloves.  
Conceptualization, R.D. and H.J.; methodology, R.D. and S.Z.; software, R.D.; validation, H.J., S.Z. and L.Q.; formal analysis, H.J.; investigation, H.J. and R.D.; resources, R.D.; data curation, R.D.; writing--original draft preparation, R.D.; writing--review and editing, R.D., H.J., L.Q., S.Z. and J.Y.; visualization, R.D.; supervision, H.J., S.Z. and L.C.; project administration, H.J. and R.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Industry-University-Research Innovation Foundation of the Chinese University (2021 LDA06003); the Provincial Graduate Student Innovation Ability Training Funding Project of Hebei Provincial Education Department (CXZZSS2023058) and Science Foundation of Hebei Normal University (L2021B31).  
Not applicable.  
The authors declare no conflict of interest.