Abduazizov Nabijon Azamatovich
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Abduazizov Nabijon Azamatovich has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Akmalbek Abdusalomov
Department of Computer Engineering, Gachon University Sujeong-Gu, Seongnam-Si 13120, Gyeonggi-Do, Republic of Korea
1 shared publication - Sabina Umirzakova
Department of Computer Science, Chungbuk National University, Cheongju 28644, Republic of Korea
1 shared publication - Sanjar Mukhamadiev
Department of Artificial Intelligence, Tashkent State University of Economics, Tashkent 100066, Uzbekistan
1 shared publication - Saydirasulov Norkobil Saydirasulovich
Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea
1 shared publication - Young-Im Cho
Department of IT Convergence Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Republic of Korea
1 shared publication - Zavqiddin Temirov
Department of Digital Technologies, Alfraganus University, Yukori Karakamish Street 2a, Tashkent 100190, Uzbekistan
1 shared publication
Latest Publications
- Open Access
Lightweight YOLOv5s Model for Early Detection of Agricultural Fires
Agricultural fires significantly threaten global food systems, ecosystems, and rural economies, necessitating timely detection to prevent widespread damage. This study presents a lightweight and enhanced version of the YOLOv5s model, optimized for early-stage agricultural fire detection. The core innovation involves deepening the C3 block and integrating DarknetBottleneck modules to extract finer visual features from subtle fire indicators such as light smoke and small flames. Experimental evaluations were conducted on a custom dataset of 3200 annotated agricultural fire images. The proposed model achieved a precision of 88.9%, a recall of 85.7%, and a mean Average Precision (mAP) of 87.3%, outperforming baseline YOLOv5s and several state-of-the-art (SOTA) detectors such as YOLOv7-tiny and YOLOv8n. The model maintains a compact size (7.5 M parameters) and real-time capability (74 FPS), making it suitable for resource-constrained deployment. Our findings demonstrate that focused architectural refinement can significantly improve early fire detection accuracy, enabling more effective response strategies and reducing agricultural losses.
Open AccessLightweight YOLOv5s Model for Early Detection of Agricultural Fires
Agricultural fires significantly threaten global food systems, ecosystems, and rural economies, necessitating timely detection to prevent widespread damage. This study presents a lightweight and enhanced version of the YOLOv5s model, optimized for early-stage agricultural fire detection. The core innovation involves deepening the C3 block and integrating DarknetBottleneck modules to extract finer visual features from subtle fire indicators such as light smoke and small flames. Experimental evaluations were conducted on a custom dataset of 3200 annotated agricultural fire images. The proposed model achieved a precision of 88.9%, a recall of 85.7%, and a mean Average Precision (mAP) of 87.3%, outperforming baseline YOLOv5s and several state-of-the-art (SOTA) detectors such as YOLOv7-tiny and YOLOv8n. The model maintains a compact size (7.5 M parameters) and real-time capability (74 FPS), making it suitable for resource-constrained deployment. Our findings demonstrate that focused architectural refinement can significantly improve early fire detection accuracy, enabling more effective response strategies and reducing agricultural losses.