Dense Small Object Detection Based on an Improved YOLOv7 Model
Xun Chen, Linyi Deng, Chao Hu, Tianyi Xie, Chengqi Wang
Abstract
Detecting small and densely packed objects in images remains a significant challenge in computer vision. Existing object detection methods often exhibit low accuracy and frequently miss detection when identifying dense small objects and require larger model parameters. This study introduces a novel detection framework designed to address these limitations by integrating advanced feature fusion and optimization techniques. Our approach focuses on enhancing both detection accuracy and parameter efficiency. The approach was evaluated on the open-source VisDrone2019 data set and compared with mainstream algorithms. Experimental results demonstrate a 70.2% reduction in network parameters and a 6.3% improvement in [email protected] over the original YOLOv7 algorithm. These results demonstrate that the enhanced model surpasses existing algorithms in detecting small objects.