Hierarchical Mask2Former: Panoptic Segmentation of Crops, Weeds and Leaves

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2017

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M. Di Cicco, C. Potena, G. Grisetti, and A. Pretto, “Automatic model based dataset generation for fast and accurate crop and weeds detection,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 5188–5195

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2017

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A. Kirillov, K. He, R. Girshick, C. Rother, and P. Dollár, “Panoptic segmentation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 9404–9413

2019

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K. Osorio, A. Puerto, C. Pedraza, D. Jamaica, and L. Rodríguez, “A deep learning approach for weed detection in lettuce crops using multispectral images,” AgriEngineering , vol. 2, no. 3, pp. 471–488, 2020

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N. Genze, R. Ajekwe, Z. Güreli, F. Haselbeck, M. Grieb, and D. G. Grimm, “Deep learning-based early weed segmentation using motion blurred UAV images of sorghum fields,” Computers and Electronics in Agriculture , vol. 202, p. 107388, 2022

2022

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B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar, “Masked-attention mask transformer for universal image segmentation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 1290–1299

2022

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2022

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J. Weyler, F. Magistri, P. Seitz, J. Behley, and C. Stachniss, “In-field phenotyping based on crop leaf and plant instance segmentation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2022, pp. 2725–2734