Azimjon Akhtamov
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Azimjon Akhtamov has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
Aziz Nasridinov
Department of Computer Science, Chungbuk National University, Cheongju, 28644, South Korea
1 shared publication- Sang Hyun Choi
Department of Bigdata, Chungbuk National University, Cheongju 28644, Republic of Korea
1 shared publication
Latest Publications
- Open Access
- 1 August 2025
- Published by Association for the Advancement of Artificial Intelligence (AAAI) in Proceedings of the AAAI Symposium Series
- Vol. 6 (1) , 75-77
A Multimodal Fusion Model for Enhanced Industrial Glove-Wearing Compliance Detection
Glove detection in manufacturing environments is challenging due to glove-background blending and limited dataset diversity. To address this, we propose a multimodal detection framework that enhances segmentation models through wrist keypoint-guided feature fusion, effectively reducing false negatives. We also introduce a unified dataset spanning five manufacturing domains to improve generalizability. Experimental results show our method achieves mAP 0.821, outperforming the baseline YOLOv11-Seg (mAP 0.792). This highlights the effectiveness of feature fusion between segmentation and keypoints for accurate and reliable glove compliance monitoring in industrial settings.
- 1 August 2025
- Published by Association for the Advancement of Artificial Intelligence (AAAI) in Proceedings of the AAAI Symposium Series
- Vol. 6 (1) , 75-77
Open AccessA Multimodal Fusion Model for Enhanced Industrial Glove-Wearing Compliance Detection
Glove detection in manufacturing environments is challenging due to glove-background blending and limited dataset diversity. To address this, we propose a multimodal detection framework that enhances segmentation models through wrist keypoint-guided feature fusion, effectively reducing false negatives. We also introduce a unified dataset spanning five manufacturing domains to improve generalizability. Experimental results show our method achieves mAP 0.821, outperforming the baseline YOLOv11-Seg (mAP 0.792). This highlights the effectiveness of feature fusion between segmentation and keypoints for accurate and reliable glove compliance monitoring in industrial settings.