Year: 2023
Type: conference-paper
Abstract: Small object detection has long been a difficulty and research hotspot in computer vision. Driven by deep learning, small object detection has made major breakthroughs and has been successfully used in fields such as national defense security, intelligent transportation, and industrial automation. In our research, we conduct a comprehensive analysis and improvement of the YOLOv8-n algorithm for object detection, focusing on the SE Attention and detection heads of small object. Through detailed ablation studies to assess its contribution to model performance, each strategy is systematically evaluated individually and collectively. The results show that each strategy uniquely enhances the performance of the model, significantly improving mAP when the two strategies are integrated
Authors: Hao, Wang, Ablameyko, Sergey
Institution: Belarusian State University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7019775979