# Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion
## 1 Introduction  
Amid the rapid pace of global urbanization, the generation of urban municipal garbage has increased exponentially, presenting unprecedented challenges to environmental protection and resource recovery. Traditional manual garbage classification methods, burdened by inefficiencies, high costs, and susceptibility to subjective biases, are increasingly inadequate to meet the demands of modern cities for efficient and precise garbage management within the framework of sustainable development. Consequently, deep learning-based intelligent garbage classification technologies, renowned for their superior performance in feature extraction and pattern recognition, have emerged as a pivotal and challenging research focus in recent years. Early studies primarily focused on leveraging classical convolutional neural networks and their variants to enhance feature extraction and classification performance for garbageimagery. For instance, [PERSON] et al. enhanced the MobileNetV2 model by incorporating an attention mechanism and extended its generalization capability via transfer learning, achieving a 90.7% classification accuracy on the Huawei Cloud dataset [1]. [PERSON] et al. fused the local feature extraction strength of ResNet with the global information capture of the Vision Transformer, further integrating Pyramid Pooling Module (PPM) and Convolutional Block Attention Module (CBAM) modules to improve both performance and accuracy in garbage classification [2]. Additionally, [PERSON] and [PERSON] employed an improved Single Shot MultiBox Detector (SSD) neural network, attaining a garbage detection accuracy of approximately 87%, thereby demonstrating the potential of object detection methods in this domain [3]. Furthermore, [PERSON] et al. combined the deep features extracted from a 50-layer Residual Network (ResNet50) with traditional classification algorithms such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest, achieving a garbage classification performance of 91% [4]. [PERSON] et al. built upon a 16-layer VGG network (VGG-16) by integrating content and boundary-aware mechanisms, which elevated the accuracy to 98% [5]. [PERSON] et al., after comparing various classical convolutional neural network models, recommended the utilization of ResNet18 [6]. Similarly, both [PERSON] et al. and [PERSON] et al. optimized the MobileNetV2 architecture, each reporting classification accuracies exceeding 98% on their respective custom garbage datasets [7; 8]. [PERSON] et al. employed a compact convolutional neural network in conjunction with an adaptive image enhancement strategy, achieving high classification accuracy on a custom garbage dataset [9]. Concurrently, [PERSON] et al. exploited the advanced architecture of MobileNetV3-Large by incorporating depthwise separable convolutions, inverted residual structures, lightweight attention mechanisms, and the hard_swish activation function to facilitate deep recognition of garbage images, attaining an accuracy of 81% [10]. [PERSON] et al. enhanced Inception V3 and integrated multi-source data, which resulted in a 4.80% improvement in classification accuracy on a real-world collected dataset [11]. Additionally, [PERSON] et al. introduced feature fusion and depthwise separable convolutions into an improved ResNet-50, achieving an accuracy of 94.13% on the TrashNet dataset [12]. While the aforementioned methods based on conventional convolutional neural network architectures have achieved remarkable success, challenges such as complex backgrounds, multi-label scenarios, and fine-grained classification in garbage images persist. To address these issues, some researchers have started exploring hybrid model designs to fully leverage the complementary advantages of diverse models. In this context, [PERSON] et al. developed the Selective Enhanced Feature Weighted Attention Module (SEFWaM) framework, which employs transfer learning and algorithm fusion to achieve a 94.2% accuracy on the Trashnet 2.0 dataset [13]. [PERSON] et al.