Segmentation and detection from organised 3D point clouds: a case study in broccoli head detection

Le Louedec, Justin, Montes, Hector, Duckett, Tom and Cielniak, Grzegorz (2020) Segmentation and detection from organised 3D point clouds: a case study in broccoli head detection. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).


Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


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.

Keywords:real-world datasets, RGBD sensors, CNNs, 3D vision approaches, Convolutional Neural Networks, shape analysis, occlusion handling, size estimation, plant growth, sensing technologies, lighting conditions, agriculture, autonomous harvesting, broccoli head detection, organised 3D point clouds
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:45041
Deposited On:22 Jul 2021 10:36

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