Le Louedec, Justin, Montes, Hector A., 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), 14-19 June 2020, Seattle, WA, USA, USA.
Full content URL: https://doi.org/10.1109/CVPRW50498.2020.00040
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Le_Louedec_Segmentation_and_Detection_From_Organised_3D_Point_Clouds_A_Case_CVPRW_2020_paper.pdf - Whole Document 2MB |
Item Type: | Conference or Workshop contribution (Paper) |
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Item Status: | Live Archive |
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.
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 |
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Subjects: | G Mathematical and Computer Sciences > G700 Artificial Intelligence G Mathematical and Computer Sciences > G400 Computer Science D Veterinary Sciences, Agriculture and related subjects > D415 Crop Production D Veterinary Sciences, Agriculture and related subjects > D400 Agriculture G Mathematical and Computer Sciences > G740 Computer Vision |
Divisions: | College of Science > School of Computer Science |
Related URLs: | |
ID Code: | 43425 |
Deposited On: | 20 Jan 2021 10:34 |
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