Multi-Label Image Classification Based on Object Detection and Dynamic Graph Convolutional Networks

Multi-label image classification is recognized as an important task within the field of computer vision, a discipline that has experienced a significant escalation in research endeavors in recent years. The widespread adoption of convolutional neural networks (CNNs) has catalyzed the remarkable success of architectures such as ResNet-101 within the domain of image classification. However, in multi-label image classification tasks, it is crucial to consider the correlation between labels. In order to improve the accuracy and performance of multi-label classification and fully combine visual and semantic features, many existing studies use graph convolutional networks (GCN) for modeling. Object detection and multi-label image classification exhibit a degree of conceptual overlap; however, the integration of these two tasks within a unified framework has been relatively underexplored in the existing literature. In this paper, we come up with Object-GCN framework, a model combining object detection network YOLOv5 and graph convolutional network, and we carry out a thorough experimental analysis using a range of well-established public datasets. The designed framework Object-GCN achieves significantly better performance than existing studies in public datasets COCO2014, VOC2007, VOC2012. The final results achieved are 86.9%, 96.7%, and 96.3% mean Average Precision (mAP) across the three datasets.