Work

Dense Small Object Detection Based on an Improved YOLOv7 Model

Year: 2024

Type: article

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.

Source: Applied Sciences

DOI: 10.3390/app14177665

Authors: Xun Chen, Linyi Deng, Chao Hu, Tianyi Xie, Chengqi Wang

Institutions: Hainan University, Central South University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funders: National Natural Science Foundation of China, Central South University, Hainan University, National Key Research and Development Program of China