Garbage detection and classification are essential for promoting the green economy and achieving a low-carbon circular economy. However, current models face challenges such as large parameters and high computational costs, limiting their deployment on resource-constrained devices. To address these issues, a lightweight GCAW-YOLOv8n model is proposed that balances model size and detection accuracy. Firstly, the C3Ghost and GhostConv modules from GhostNet are integrated into the YOLOv8n backbone to reduce parameters. Secondly, the context anchor attention is introduced to enhance feature extraction and detection accuracy. Then, the asymptotic feature pyramid network is used to improve multi-scale detection, and the WIoU v3 loss function optimizes bounding box regression. Finally, the improved model is validated using the Taco dataset and a custom dataset. Experimental results show that, compared with the original YOLOv8n model, the GCAW-YOLOv8n model reduces parameters by 14.3% and floating-point operations by 33.3%, while precision and recall increase by 4.4% and 1.9%, respectively. The mAP@0.5 improves to 81.3%, a 0.7% gain. This model achieves a better balance between lightweight design and detection accuracy, making it suitable for deployment in edge devices for garbage detection.