ZHU Yongchang,YANG Sen,TONG Jigang,WANG Zenghui.Multi-scale detector optimized for small target[J].Optoelectronics Letters,2024,(4):243-248 Multi-scale detector optimized for small target Author Name Affiliation ZHU Yongchang  School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China  YANG Sen  School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China  TONG Jigang  School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China  WANG Zenghui  Center of Artificial Intelligence and Data Science, University of South Africa, Florida 1709, South Africa Department of Electrical Engineering, University of South Africa, Florida 1709, South Africa  Abstract:        The effectiveness of deep learning networks in detecting small objects is limited, thereby posing challenges in addressing practical object detection tasks. In this research, we propose a small object detection model that operates at multiple scales. The model incorporates a multi-level bidirectional pyramid structure, which integrates deep and shallow networks to simultaneously preserve intricate local details and augment global features. Moreover, a dedicated multi-scale detection head is integrated into the model, specifically designed to capture crucial information pertaining to small objects. Through comprehensive experimentation, we have achieved promising results, wherein our proposed model exhibits a mean average precision (mAP) that surpasses that of the well-established you only look once version 7 (YOLOv7) model by 1.1%. These findings validate the improved performance of our model in both conventional and small object detection scenarios. Hits: 0 Download times: 0 View Full Text    Download reader