A 3D Light weight neural network for plant part segmentation and architectural trait extraction

With the increase in research cases of the application of a convolutional neural network (CNN)-based object detection technology, studies on the light-weight CNN models that can be performed in real time on the edge-computing devices are also increasing. This paper proposed scalable convolutional blocks that can be easily designed CNN networks of You Only Look Once (YOLO) detector which have the balanced processing speed and accuracy of the target edge-computing devices considering different performances by exchanging the proposed blocks simply. The maximum number of kernels of the convolutional layer was determined through simple but intuitive speed comparison tests for three edge-computing devices to be considered. The scalable convolutional blocks were designed in consideration of the limited maximum number of kernels to detect objects in real time on these edge-computing devices. Three scalable and fast YOLO detectors (SF-YOLO) which designed using the proposed scalable convolutional blocks compared the processing speed and accuracy with several conventional light-weight YOLO detectors on the edge-computing devices. When compared with YOLOv3-tiny, SF-YOLO was seen to be 2 times faster than the previous processing speed but with the same accuracy as YOLOv3-tiny, and also, a 48% improved processing speed than the YOLOv3-tiny-PRN which is the processing speed improvement model. Also, even in the large SF-YOLO model that focuses on the accuracy performance, it achieved a 10% faster processing speed with better accuracy of 40.4% [email protected] in the MS COCO dataset than YOLOv4-tiny model.

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Licence Plate Identification and Recognition for Non-Helmeted Motorcyclists using Light-weight Convolution Neural Network

2020 International Conference for Emerging Technology (INCET) ◽

10.1109/incet49848.2020.9154075 ◽

2020 ◽

Author(s):

Meghal Darji ◽

Jaivik Dave ◽

Nadim Asif ◽

Chirag Godawat ◽

Vishal Chudasama ◽

...

Keyword(s):

Neural Network ◽

Convolution Neural Network ◽

Light Weight

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Boosting plant-part segmentation of cucumber plants by enriching incomplete 3D point clouds with spectral data

Biosystems Engineering ◽

10.1016/j.biosystemseng.2021.09.004 ◽

2021 ◽

Vol 211 ◽

pp. 167-182

Author(s):

Frans P. Boogaard ◽

Eldert J. van Henten ◽

Gert Kootstra

Keyword(s):

Spectral Data ◽

Point Clouds ◽

Plant Part ◽

3D Point Clouds ◽

Part Segmentation

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Hyperspectral imaging and artificial intelligence to detect oral malignancy – part 1 - automated tissue classification of oral muscle, fat and mucosa using a light-weight 6-layer deep neural network

Head & Face Medicine ◽

10.1186/s13005-021-00292-0 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Daniel G. E. Thiem ◽

Paul Römer ◽

Matthias Gielisch ◽

Bilal Al-Nawas ◽

Martin Schlüter ◽

...

Keyword(s):

Neural Network ◽

Computer Vision ◽

Deep Learning ◽

Hyperspectral Imaging ◽

Oral Mucosa ◽

Deep Neural Network ◽

Light Weight ◽

Tissue Classification ◽

Raw Data

Abstract Background Hyperspectral imaging (HSI) is a promising non-contact approach to tissue diagnostics, generating large amounts of raw data for whose processing computer vision (i.e. deep learning) is particularly suitable. Aim of this proof of principle study was the classification of hyperspectral (HS)-reflectance values into the human-oral tissue types fat, muscle and mucosa using deep learning methods. Furthermore, the tissue-specific hyperspectral signatures collected will serve as a representative reference for the future assessment of oral pathological changes in the sense of a HS-library. Methods A total of about 316 samples of healthy human-oral fat, muscle and oral mucosa was collected from 174 different patients and imaged using a HS-camera, covering the wavelength range from 500 nm to 1000 nm. HS-raw data were further labelled and processed for tissue classification using a light-weight 6-layer deep neural network (DNN). Results The reflectance values differed significantly (p < .001) for fat, muscle and oral mucosa at almost all wavelengths, with the signature of muscle differing the most. The deep neural network distinguished tissue types with an accuracy of > 80% each. Conclusion Oral fat, muscle and mucosa can be classified sufficiently and automatically by their specific HS-signature using a deep learning approach. Early detection of premalignant-mucosal-lesions using hyperspectral imaging and deep learning is so far represented rarely in in medical and computer vision research domain but has a high potential and is part of subsequent studies.

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