To address the issues of high model parameter counts, large computational demands, and the difficulty of embedding garbage detection models into edge devices, a lightweight YOLOv8n-Garbage model was proposed. Firstly, a ContextGuided Block was introduced to construct the C2f-ContextGuided module, enhancing the joint utilization of local and global contextual information. Additionally, wavelet pooling was incorporated to create wavelet pooling-convolution (WP-Conv), which better captures detailed information such as shape, texture, and color, thereby improving feature extraction capabilities. Secondly, a weighted bidirectional feature pyramid network (BiFPN) was introduced to replace the original model’s Neck section. By optimizing the multi-scale feature fusion mechanism, BiFPN significantly improves the model’s performance in small object detection tasks. Finally, a detection head based on shared convolution (DHSC) was designed through shared convolution, achieving both model lightweighting and accuracy improvement. The results show that YOLOv8n-Garbage reduces the parameter count and floating-point operations by 71.4% and 49.4%, respectively, compared to the original YOLOv8n model, while increasing the mAP@0.5 by 0.3%. YOLOv8n-Garbage demonstrates significant advantages in garbage classification detection, providing technical support for intelligent urban waste management.