Journal of South China University of Technology(Natural Science) >

2025 , Vol. 53 >Issue 7: 60 - 69

DOI: https://doi.org/10.12141/j.issn.1000-565X.240591

Electronics, Communication & Automation Technology

Real-Time Feeding Target Recognition Method Based on SAM Optimization

  • ZHANG Qin ,
  • WENG Kaihang
Expand
  • School of Mechanical and Automotive Engineering，South China University of Technology，Guangzhou 510640，Guangdong，China
张勤（1964—），女，博士，教授，主要从事机器人及其应用研究。E-mail： zhangqin@scut.edu.cn

Received date: 2024-12-20

  Online published: 2025-02-25

Supported by

the Natural Science Foundation of Hainan Province(324MS095)

Fold

Abstract

Feeding-assistance robots are key equipment in promoting the modernization and transformation of animal 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 performance 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 recognition 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.

Key words： feeding-assistance robot; segment anything model; cow feeding; target recognition; segmentation accuracy

Cite this article

ZHANG Qin , WENG Kaihang . Real-Time Feeding Target Recognition Method Based on SAM Optimization[J]. Journal of South China University of Technology(Natural Science), 2025 , 53(7) : 60 -69 . DOI: 10.12141/j.issn.1000-565X.240591

References

[1] 张勤，任海林，胡嘉辉 ．基于信息融合的智能推料机器人设计与试验［J］．农业机械学报，2022，54（6）：78-84，93．
  ZHANG Qin， REN Hailin， HU Jiahui ．Design and experiment of intelligent feed-pushing robot based on information fusion［J］．Transactions of the Chinese Society for Agricultural Machinery，2022，54（6）：78-84，93．
[2] 张勤，胡嘉辉，任海林 ．饲喂辅助机器人的智能推料方法与试验研究［J］．华南理工大学学报（自然科学版），2022，50（6）：111-120．
  ZHANG Qin， HU Jiahui， REN Hailin ．Intelligent pushing method and experiment of feeding assistant robot［J］．Journal of South China University of Technology（Natural Science Edition），2022，50（6）：111-120．
[3] ALBRIGHT J L ．Feeding behavior of dairy cattle［J］．Journal of Dairy Science，1993，76（2）：485-498.
[4] STANKOVSKI S， OSTOJIC G， SENK I，et al ．Dairy cow monitoring by RFID［J］．Scientia Agricola，2012，69：75-80．
[5] PORTO S M C， ARCIDIACONO C， GIUMMARRA A，et al ．Localisation and identification performances of a real-time location system based on ultra wide band technology for monitoring and tracking dairy cow beha-viour in a semi-open free-stall barn［J］．Computers and Electronics in Agriculture，2014，108：221-229．
[6] 姜威，郭庆，司陈，等 ．一种用于奶牛定位的装置和方法：CN201310228400.3［P］．2013-09-18．
[7] DHANYA V G， SUBEESH A， KUSHWAHA N L，et al ．Deep learning based computer vision approaches for smart agricultural applications［J］．Artificial Intelligence in Agriculture，2022，6：211-229．
[8] DIWAN T， ANIRUDH G， TEMBHURNE J V ．Object detection using YOLO：challenges，architectural succe-ssors，datasets and applications［J］．Multimedia Tools and Applications，2023，82（6）：9243-9275．
[9] KUAN C Y， TSAI Y C， HSU J T，et al ．An imaging system based on deep learning for monitoring the feeding behavior of dairy cows［C］∥ Proceedings of 2019 ASABE Annual International Meeting．Boston：American Society of Agricultural and Biological Engineers，2019：1/1-9．
[10] JIANG B， WU Q， YIN X，et al ．FLYOLOv3 deep learning for key parts of dairy cow body detection［J］．Computers and Electronics in Agriculture，2019，166：104982/1-8．
[11] BEZEN R， EDAN Y， HALACHMI I ．Computer vision system for measuring individual cow feed intake using RGB-D camera and deep learning algorithms［J］．Computers and Electronics in Agriculture，2020，172：105345/1-11．
[12] WANG J， ZHANG X， GAO G，et al ．Open pose mask R-CNN network for individual cattle recognition［J］．IEEE Access，2023，11：113752-113768．
[13] LI X， GAO R， LI Q，et al ．Multi-target feeding-behavior recognition method for cows based on improved RefineMask［J］．Sensors，2024，24（10）：2975/1-19．
[14] BOLYA D， ZHOU C， XIAO F，et al ．YOLACT：real-time instance segmentation［C］∥ Proceedings of 2019 IEEE/CVF International Conference on Computer Vision．Seoul：IEEE，2019：9156-9165．
[15] YANG G， LI R， ZHANG S，et al ．Extracting cow point clouds from multi-view RGB images with an improved YOLACT++ instance segmentation［J］．Expert Systems with Applications，2023，230：120730/1-14．
[16] KIRILLOV A， MINTUN E， RAVI N，et al ．Segment anything［C］∥ Proceedings of 2023 IEEE/CVF International Conference on Computer Vision．Paris：IEEE，2023：3992-4003．
[17] ZHANG J ．Research on derived tasks and realistic applications of segment anything model：a literature review［J］．Frontiers in Computing and Intelligent Systems，2023，5（2）：116-119．
[18] YANG X， DAI H， WU Z，et al ．An innovative segment anything model for precision poultry monitoring ［J］．Computers and Electronics in Agriculture，2024，222：109045/1-12．
[19] LI Q， ZHAO J， BAI D，et al ．Cattle body size measurement system based on dual-position cameras［C］∥ Proceedings of 2024 International Conference on Computer Vision and Deep Learning．Changsha：ACM，2024：47/1-6．
[20] CHEN K， LIU C， CHEN H，et al ．RSPrompter：learning to prompt for remote sensing instance segmentation based on visual foundation model［J］．IEEE Tran-sactions on Geoscience and Remote Sensing，2024，62：4701117/1-17．
[21] DOSOVITSKIY A， BEYER L， KOLESNIKOV A，et al ．An image is worth 16　×　16 words：transformers for image recognition at scale［C］∥ Proceedings of the 9th International Conference on Learning Representations．［S.l.］：OpenReview.net，2021：1-21．
[22] XIONG Y， VARADARAJAN B， WU L，et al ．EfficientSAM：leveraged masked image pretraining for efficient segment anything［C］∥ Proceedings of 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition．Seattle：IEEE，2024：16111-16121．
[23] 叶峰，陈彪，赖乙宗 ．基于特征空间嵌入的对比知识蒸馏算法［J］．华南理工大学学报（自然科学版），2023，51（5）：13-23．
  YE Feng， CHEN Biao， LAI Yizong ．Contrastive knowledge distillation method based on feature space embedding［J］．Journal of South China University of Technology （Natural Science Edition），2023，51（5）：13-23．
[24] KE L， YE M， DANELLJAN M，et al ．Segment anything in high quality［C］∥ Advances in Neural Information Processing Systems 36：37th Conference on Neural Information Processing Systems．San Diego：Neural Information Processing Systems Foundation，Inc.，2023：29914-29934．
[25] LYU C， ZHANG W， HUANG H，et al ．RTMDet：an empirical study of designing real-time object detectors ［EB/OL］．（2022-12-16）［2024-11-25］．．
[26] LI Y， MAO H， GIRSHICK R，et al ．Exploring plain vision transformer backbones for object detection［C］∥Proceedings of the 17th European Conference on Computer Vision．Tel Aviv：Springer，2022：280-296．
[27] TIAN Y， SU D， LAURIA S，et al ．Recent advances on loss functions in deep learning for computer vision ［J］．Neurocomputing，2022，497：129-158．
[28] HE K， GKIOXARI G， DOLLáR P，et al ．Mask R-CNN［C］∥ Proceedings of 2017 IEEE International Conference on Computer Vision．Venice：IEEE，2017：2961-2969．
[29] WANG X， KONG T， SHEN C，et al ．SOLO： segmenting objects by locations［C］∥ Proceedings of the 16th European Conference on Computer Vision．Glasgow：Springer，2020：649-665.
[30] WANG X， ZHANG R， KONG T，et al ．SOLOv2：dynamic and fast instance segmentation［C］∥ Advances in Neural Information Processing Systems 33：34th Conference on Neural Information Processing Systems．San Diego：Neural Information Processing Systems Foundation，Inc.， 2020：17721-17732．
Options
Outlines

/

〈 〉