# Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion
## 1 Introduction
achieved a detection accuracy of 98.5% on underwater images using an enhanced YOLOv8 [33]. [PERSON] et al. further advanced the field by improving SSD and the feature fusion module, replacing VGG16 with ResNet-101 and, thereby, surpassing the performance of several existing object detection algorithms [34]. However, when confronted with the complexities and variabilities of real-world scenarios, single-model approaches or static feature extraction techniques still exhibit deficiencies in both generalization and real-time performance. Traditional feature extraction and fusion strategies often fail to fully capture the complementary information across different modalities, thereby limiting their effectiveness in handling complex backgrounds and fine-grained distinctions. Moreover, most attention mechanisms rely on static designs that are poorly equipped to adapt to dynamic changes in data distribution, which in turn undermines the model's generalization capabilities in small-sample and extreme scenarios. Additionally, the majority of existing research continues to depend on conventional convolutional neural network architectures, missing opportunities to exploit the inter-sample relational information--most notably, the latent advantages of graph neural networks (GNNs) remain largely unexplored in the realm of garbage classification. To address these challenges, this paper proposes an innovative garbage classification method, with its primary contributions including the following:  
Multimodal Feature Fusion: An enhanced RNAM network model is employed to extract deep semantic features, while LAB histograms are utilized to capture color distribution information. This complementary integration of diverse modalities effectively bolsters the discriminative power of the feature representation. Dynamic Graph Construction: Based on the fused features, a graph structure is constructed using a k-nearest neighbor algorithm. The value of k is dynamically adjusted to ensure the coherence and validity of the graph structure across varying batches, thereby effectively capturing local neighborhood relationships. Dynamic Graph Neural Network Design: A Graph Attention Network v2 Convolution (GATv2 Conv) layer is integrated into the graph structure, leveraging multi-head attention to adaptively aggregate neighborhood information. Concurrently, feature projection, batch normalization, and dropout are employed to alleviate the issue of heterogeneous feature scale mismatches, thereby effectively capturing both local and global relational features to further enhance the model's capability for garbage image recognition. The remainder of this paper is organized as follows. In Section 2, we provide an in-depth discussion of the multimodal feature fusion strategy for image preprocessing and feature enhancement. Section 3 elaborates on the graph construction process and details the implementation of the dynamic graph neural network. In Section 4, the design of the loss function and the corresponding optimization strategies are presented. Section 5 introduces and discusses the experimental results, and finally, Section 6 summarizes the main contributions of this work and outlines potential future research directions.