为解决垃圾自动分拣难题，设计了以人工智能计算平台Jetson NANO和YOLOv8算法为基础的生活垃圾智能分拣系统。首先，采用轻量化YOLOv8多目标检测算法将垃圾分为四大类并实现在有遮挡情况下的准确识别；其次，基于CoreXY坐标变换算法实现机械爪抓取的最佳角度判断与垃圾精准抓取；最后，基于TensorRT优化技术实现轻量化YOLOv8多目标检测算法在NANO硬件平台的部署，完成基于YOLOv8-NANO的四大类垃圾智能分拣系统整体构建。实验验证结果表明：在自制的包含四大类共13小类垃圾测试中，分拣系统可以有效实现单目标与多目标的检测，检测mAP0.5：0.95（当IoU阈值在0.5 ~0.95逐步增加时的平均精度）为97.3%，目标检测速度达到30.6帧／s，垃圾分拣速度最快可达6件／min。

In order to solve the problem of automatic sorting of garbage, an intelligent sorting method for household garbage was designed based on the artificial intelligence computing platform Jetson NANO and YOLOv8 algorithm. Firstly, the lightweight YOLOv8 multi-target detection algorithm was used to classify garbage into four categories and achieve accurate recognition in the presence of occlusion. Secondly, the CoreXY coordinate transformation algorithm was used to determine the optimal angle for mechanical claw grasping and achieve precise garbage grasping. Finally, based on TensorRT optimization technology, the lightweight YOLOv8 multi-objective detection algorithm was deployed on the NANO hardware platform, completing the overall construction of the four major categories of garbage intelligent sorting system based on YOLOv8-NANO. The results show that in the self-made garbage test consisting of four major categories and a total of 13 sub categories, the sorting system can effectively achieve detection of single and multiple targets. The detection mAP0.5:0.95 value is 97.3%, and the target detection speed reaches 30.6 frames/s. The fastest garbage sorting speed could reach 6 pieces/min.