MHA-DGCLN: multi-head attention-driven dynamic graph convolutional lightweight network for multi-label image classification of kitchen waste

Kitchen waste images encompass a wide range of garbage categories, posing a typical multi-label classification challenge. However, due to the complex background and significant variations in garbage morphology, there is currently limited research on kitchen waste classification. In this paper, we propose a multi-head attention-driven dynamic graph convolution lightweight network for multi-label classification of kitchen waste images. Firstly, we address the issue of large model parameterization in traditional GCN methods by optimizing the backbone network for lightweight model design. Secondly, to overcome performance losses resulting from reduced model parameters, we introduce a multi-head attention mechanism to mitigate feature information loss, enhancing the feature extraction capability of the backbone network in complex scenarios and improving the correlation between graph nodes. Finally, the dynamic graph convolution module is employed to adaptively capture semantic-aware regions, further boosting recognition capabilities. Experiments conducted on our self-constructed multi-label kitchen waste classification dataset MLKW demonstrate that our proposed algorithm achieves a 8.6% and 4.8% improvement in mAP compared to the benchmark GCN-based methods ML-GCN and ADD-GCN, respectively, establishing state-of-the-art performance. Additionally, extensive experiments on two public datasets, MS-COCO and VOC2007, showcase excellent classification results, highlighting the strong generalization ability of our algorithm.