ScholarWorks@경상국립대학교

초록

Recent object detection research has primarily focused on achieving high accuracy, often overlooking model efficiency and size. To address this issue, we propose the Multi-SpatialLite Attention (MSA) YOLOv5 model, which emphasizes a balance between accuracy and model size. The proposed model was trained on the VHR-10 dataset for small object detection, achieving a lightweight design while maintaining excellent performance in small object detection. Experimental results show that the proposed model achieved 97.6% precision, 99.0% recall, and 97.0% F1-score, while maintaining a lightweight structure with 1.795 million parameters and demonstrating outstanding performance with a mean Average Precision (mAP) of 98.3%. These results suggest the model's ability to enhance precise detection of small objects and provide effective real-time performance even in limited hardware environments. Notably, it has surpassed existing models in applications requiring real-time performance in constrained hardware environments. © 2024 IEEE.

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