DS Journal of Artificial Intelligence and Robotics (DS-AIR)

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Volume 1 | Issue 2 | Year 2023 | Article Id: AIR-V1I2P102 DOI: https://doi.org/10.59232/AIR-V1I2P102

Mask the Target: Mask R-CNN-Based Approach for Open-Range Individual Cattle Segmentation

Rotimi-Williams Bello, Mojisola Abosede Oladipo

ReceivedRevisedAcceptedPublished
09 Jul 202330 Aug 202314 Sep 202311 Oct 2023

Citation

Rotimi-Williams Bello, Mojisola Abosede Oladipo. “Mask the Target: Mask R-CNN-Based Approach for Open-Range Individual Cattle Segmentation .” DS Journal of Artificial Intelligence and Robotics, vol. 1, no. 2, pp. 15-25, 2023.

Abstract

Targeting individual cattle in an open-range has been a computer vision problem with great agricultural implications for animal health monitoring, behavior monitoring, and economic benefit. In this paper, the individual cattle targets in the open-range are segmented using Mask R-CNN’s detect and segment approach. We evaluate the performance of the model proposed in this study using the Intersection Over Union (IOU) threshold of 0.5, Average Precision (AP) and mean Average Precision (mAP). The results of the experiment conducted with the Acquired dataset in this study show that the proposed model achieves an accuracy of 95%, thereby affirming the potential of the Mask R-CNN model to perform competitively with any other existing object detection and instance segmentation models for cattle target segmentation with high accuracy and average precision.

Keywords

Cattle, Mask R-CNN, Open-range, Segmentation, Target.

References

[1] Rajendra P. Sishodia, Ram L. Ray, and Sudhir K. Singh, “Applications of Remote Sensing in Precision Agriculture: A Review,” Remote Sensing, vol. 12, no. 19, pp. 1-31, 2020.

[CrossRef] [Google Scholar] [Publisher Link]

[2] Subramania Ananda Kumar, and Paramasivam Ilango, “The Impact of Wireless Sensor Network in the Field of Precision Agriculture: A Review,” Wireless Personal Communications, vol. 98, pp. 685-698, 2018.

[CrossRef] [Google Scholar] [Publisher Link]

[3] William Andrew et al., “Automatic Individual Holstein Friesian Cattle Identification via Selective Local Coat Pattern Matching in RGB-D Imagery,” 2016 IEEE International Conference on Image Processing (ICIP), pp. 484-488, 2016.

[CrossRef] [Google Scholar] [Publisher Link]

[4] Gu Jingqiu et al., “Cow Behaviour Recognition Based on Image Analysis and Activities,” International Journal of Agricultural and Biological Engineering, vol. 10, no. 3, pp. 165-174, 2017.

[Google Scholar] [Publisher Link]

[5] William Andrew, Colin Greatwood, and Tilo Burghardt, “Visual Localisation and Individual Identification of Holstein Friesian Cattle via Deep Learning,” 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2850-2859, 2017.

[CrossRef] [Google Scholar] [Publisher Link]

[6] Gullal Singh Cheema, and Saket Anand, “Automatic Detection and Recognition of Individuals in Patterned Species,” Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 27-38, 2017.

[CrossRef] [Google Scholar] [Publisher Link]

[7] Thi Thi Zin et al., “Image Technology Based Cow Identification System Using Deep Learning,” Proceedings of the International MultiConference of Engineers and Computer Scientists, pp. 1-4, 2018.

[Google Scholar] [Publisher Link]

[8] Alberto Rivas et al., “Detection of Cows Using Drones and Convolutional Neural Networks,” Sensors, vol. 18, no. 7, pp. 1-15, 2018.

[CrossRef] [Google Scholar] [Publisher Link]

[9] Kaixuan Zhao et al., “Individual Identification of Holstein Dairy Cows Based on Detecting and Matching Feature Points in Body Images,” Biosystems Engineering, vol. 181, pp. 128-139, 2019.

[CrossRef] [Google Scholar] [Publisher Link]

[10] He Liu, Amy R. Reibman, and Jacquelyn P. Boerman, “A Cow Structural Model for Video Analytics of Cow Health,” arXiv, pp. 1-13, 2020.

[CrossRef] [Google Scholar] [Publisher Link]

[11] Hengqi Hu et al., “Cow Identification Based on Fusion of Deep Parts Features,” Biosystems Engineering, vol. 192, pp. 245-256, 2020.

[CrossRef] [Google Scholar] [Publisher Link]

[12] Joseph Redmon et al., “You Only Look Once: Unified, Real-Time Object Detection,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779-788, 2016.

[CrossRef] [Google Scholar] [Publisher Link]

[13] Beibei Xu et al., “Automated Cows Counting Using Mask R-CNN in Quadcopter Vision System,” Computers and Electronics in Agriculture, vol. 171, pp. 1-12, 2020.

[CrossRef] [Google Scholar] [Publisher Link]

[14] Wen Shao et al., “Cattle Detection and Counting in UAV Images Based on Convolutional Neural Networks,” International Journal of Remote Sensing, vol. 41, no. 1, pp. 31-52, 2020.

[CrossRef] [Google Scholar] [Publisher Link]

[15] Kaiming He et al., “Mask R-CNN,” 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980-2988, 2017.

[CrossRef] [Google Scholar] [Publisher Link]

[16] Shaoqing Ren et al., “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017.

[CrossRef] [Google Scholar] [Publisher Link]

[17] Bryan C. Russell et al., “LabelMe: A Database and Web-Based Tool for Image Annotation,” International Journal of Computer Vision, vol. 77, pp. 157-173, 2008.

[CrossRef] [Google Scholar] [Publisher Link]

[18] Joseph Redmon, and Ali Farhadi, “YOLOv3: An Incremental Improvement,” arXiv, pp. 1-6, 2018.

[CrossRef] [Google Scholar] [Publisher Link]

[19] Wei Liu et al., “SSD: Single Shot Multibox Detector,” European Conference on Computer Vision, pp. 21-37, 2016.

[CrossRef] [Google Scholar] [Publisher Link]

[20] Tao Feng et al., “Cattle Target Segmentation Method in Multi-Scenes Using Improved DeepLabV3+ Method,” Animals, vol. 13, no. 15, pp. 1-15, 2023.

[CrossRef] [Google Scholar] [Publisher Link]

[21] Yongliang Qiao, Matthew Truman, and Salah Sukkarieh, “Cattle Segmentation and Contour Extraction Based on Mask R-CNN for Precision Livestock Farming,” Computers and Electronics in Agriculture, vol. 165, 2019.

[CrossRef] [Google Scholar] [Publisher Link]

[22] Tiara Lestari Subaran, Transmissia Semiawan, and Nurjannah Syakrani, “Mask R-CNN and GrabCut Algorithm for an Image-Based Calorie Estimation System,” Journal of Information Systems Engineering and Business Intelligence, vol. 8, no. 1, pp. 1-10, 2022.

[CrossRef] [Google Scholar] [Publisher Link]

[23] Jennifer Salau, and Joachim Krieter, “Instance Segmentation with Mask R-CNN Applied to Loose-Housed Dairy Cows in a Multi-Camera Setting,” Animals, vol. 10, no. 12, pp. 1-19, 2020.

[CrossRef] [Google Scholar] [Publisher Link]

[24] Venkata Sai Praveen Gunda et al., “A Hybrid Deep Learning Based Robust Framework for Cattle Identification,” 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), Bhubaneswar, India, pp. 1-5, 2022.

[CrossRef] [Google Scholar] [Publisher Link]

[25] D. Piette et al., “Individualised Automated Lameness Detection in Dairy Cows and the Impact of Historical Window Length on Algorithm Performance,” Animal, vol. 14, no. 2, pp. 409-417, 2020.

[CrossRef] [Google Scholar] [Publisher Link]

Mask the Target: Mask R-CNN-Based Approach for Open-Range Individual Cattle Segmentation