华南理工大学学报（自然科学版）2025，Vol.53Issue(7)：60-69,10.DOI:10.12141/j.issn.1000-565X.240591
基于SAM优化的饲喂目标实时识别方法
Real-Time Feeding Target Recognition Method Based on SAM Optimization
摘要
Abstract
Feeding-assistance robots are key equipment in promoting the modernization and transformation of ani-mal husbandry.The rapid and accurate identification of feeding targets is essential for enabling intelligent feed-pushing,while balancing segmentation accuracy and operational efficiency is crucial for ensuring the overall perfor-mance of recognition algorithms—an important topic in the field of intelligent livestock management.To address the mismatch between segmentation accuracy and processing efficiency in current dairy cow feeding target recogni-tion methods,this paper proposed a real-time feeding target recognition method(RTFTR)based on an optimized Segment Anything Model(SAM).Built on the SAM-det architecture,RTFTR first introduces lightweight image encoder and object detector,along with a parallelized buffer queue design,to balance the operational efficiency of each module and enhance inference speed.It then employs a High-Quality(HQ)token mechanism to enhance the feature space decoding capacity,optimizes the mask decoder,and applies stage-wise training tailored to feeding targets to improve segmentation accuracy.Experimental results show that the proposed method ensures inference efficiency while enhancing segmentation accuracy.In the task of cow feeding target recognition,the method achieves a segmentation accuracy of 98.7%for cows,96.4%for feed,99.2%for bunk,with an overall average accuracy of 98.1%,and a processing speed of 52.9 f/s,meeting the application requirements for cow feeding target recognition in complex environments and limited robotic computational resources.关键词
饲喂辅助机器人/分割大模型/奶牛饲喂/目标识别/分割精度Key words
feeding-assistance robot/segment anything model/cow feeding/target recognition/segmentation accuracy分类
信息技术与安全科学引用本文复制引用
张勤,翁凯航..基于SAM优化的饲喂目标实时识别方法[J].华南理工大学学报（自然科学版）,2025,53(7):60-69,10.基金项目
海南省自然科学基金项目(324MS095) （324MS095）
广东省现代农业产业共性关键技术研发创新团队建设项目(2019KJ129)Supported by the Natural Science Foundation of Hainan Province(324MS095) （2019KJ129）
