目的 为降低轻薄快递件(简称“轻薄件”)供件劳动强度、提高供件效率，设计了一款可代替人工的智能供件系统。方法 基于生产现场大数据集对Mask R-CNN实例分割模型进行训练，实现了轻薄件的准确识别;使用掩膜最大内切圆技术，获取堆叠轻薄件抓取区域，通过机械臂运动路径规划，实现了轻薄件的流畅抓取;基于读码器和镜面反射技术，实现了轻薄件的双面扫码。结果 实验证明，所设计的智能供件系统实现了堆叠轻薄件的精确识别和可靠抓取搬运，识别准确率达90.02%，平均抓取成功率达96.82%。结论 基于该方法构建的智能供件系统，可代替人工高效完成轻薄件供件作业。

In order to reduce the labor intensity and improve the feeding efficiency of thin and light packages, the work aims to design an intelligent feeding system that can replace manual labor. The Mask R-CNN instance segmentation model was trained based on a big dataset from production site, achieving accurate recognition of thin and light packages. The grabbing area of stacked thin and light packages was obtained by the technique of maximum inscribed circle in the package masks. Through the motion path planning of the robotic arm, smooth grabbing of thin and light packages was achieved. Double sided scanning of thin and light packages was achieved based on code readers and mirror reflection technology. The experimental results showed that the intelligent feeding system achieved accurate recognition and reliable grabbing and transporting of stacked thin and light packages, with a recognition accuracy of 90.02% and an average grabbing success rate of 96.82%. The intelligent feeding system constructed based on these methods can replace manual labor to efficiently complete the feeding of thin and light packages.