Segmentation and Detection From Organised 3D Point Clouds: A Case Study in Broccoli Head Detection

Justin Le Louedec, Hector A. Montes, Tom Duckett, Grzegorz Cielniak; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 64-65

Abstract


Autonomous harvesting is becoming an important challenge and necessity in agriculture, because of the lack of labour and the growth of population needing to be fed. Perception is a key aspect of autonomous harvesting and is very challenging due to difficult lighting conditions, limited sensing technologies, occlusions, plant growth, etc. 3D vision approaches can bring several benefits addressing the aforementioned challenges such as localisation, size estimation, occlusion handling and shape analysis. In this paper, we propose a novel approach using 3D information for detecting broccoli heads based on Convolutional Neural Networks (CNNs), exploiting the organised nature of the point clouds originating from the RGBD sensors. The proposed algorithm, tested on real-world datasets, achieves better performances than the state-of-the-art, with better accuracy and generalisation in unseen scenarios, whilst significantly reducing inference time, making it better suited for real-time in-field applications.

Related Material


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[bibtex]
@InProceedings{Louedec_2020_CVPR_Workshops,
author = {Le Louedec, Justin and Montes, Hector A. and Duckett, Tom and Cielniak, Grzegorz},
title = {Segmentation and Detection From Organised 3D Point Clouds: A Case Study in Broccoli Head Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}