scholarly journals Deep Learning Approach at the Edge to Detect Iron Ore Type

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 169
Author(s):  
Emerson Klippel ◽  
Andrea Gomes Campos Bianchi ◽  
Saul Delabrida ◽  
Mateus Coelho Silva ◽  
Charles Tim Batista Garrocho ◽  
...  
Keyword(s):  
Deep Learning ◽  
Iron Ore ◽  
Field Tests ◽  
Landslide Risk ◽  
Learning Approach ◽  
Operational Conditions ◽  
Real Time Clock ◽  
Conveyor Belts ◽  
Classification Of Images

There is a constant risk of iron ore collapsing during its transfer between processing stages in beneficiation plants. Existing instrumentation is not only expensive but also complex and challenging to maintain. In this research, we propose using edge artificial intelligence for early detection of landslide risk based on images of iron ore transported on conveyor belts. During this work, we defined the device edge and the deep neural network model. Then, we built a prototype will to collect images that will be used for training the model. This model will be compressed for use in the device edge. This same prototype will be used for field tests of the model under operational conditions. In building the prototype, a real-time clock was used to ensure the synchronization of image records with the plant’s process information, ensuring the correct classification of images by the process specialist. The results obtained in the field tests of the prototype with an accuracy of 91% and a recall of 96% indicate the feasibility of using deep learning at the edge to detect the type of iron ore and prevent its risk of avalanche.

scholarly journals Deep Learning for Multi-task Plant Phenotyping

2017 ◽  
Author(s):  
Michael P. Pound ◽  
Jonathan A. Atkinson ◽  
Darren M. Wells ◽  
Tony P. Pridmore ◽  
Andrew P. French
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Large Datasets ◽  
Natural Variability ◽  
Plant Phenotyping ◽  
Learning Approach ◽  
Learning Approaches ◽  
Classification Of Images

AbstractPlant phenotyping has continued to pose a challenge to computer vision for many years. There is a particular demand to accurately quantify images of crops, and the natural variability and structure of these plants presents unique difficulties. Recently, machine learning approaches have shown impressive results in many areas of computer vision, but these rely on large datasets that are at present not available for crops. We present a new dataset, called ACID, that provides hundreds of accurately annotated images of wheat spikes and spikelets, along with image level class annotation. We then present a deep learning approach capable of accurately localising wheat spikes and spikelets, despite the varied nature of this dataset. As well as locating features, our network offers near perfect counting accuracy for spikes (95.91%) and spikelets (99.66%). We also extend the network to perform simultaneous classification of images, demonstrating the power of multi-task deep architectures for plant phenotyping. We hope that our dataset will be useful to researchers in continued improvement of plant and crop phenotyping. With this in mind, alongside the dataset we will make all code and trained models available online.

A deep learning approach for fast detection and classification of concrete damage

2021 ◽  
Vol 128 ◽  
pp. 103785
Author(s):  
Yongqing Jiang ◽  
Dandan Pang ◽  
Chengdong Li
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Fast Detection ◽  
Concrete Damage

scholarly journals A Deep Learning Approach for the Classification of Neuronal Cell Types

2018 ◽  
Author(s):  
Alessio P. Buccino ◽  
Torbjorn V. Ness ◽  
Gaute T. Einevoll ◽  
Gert Cauwenberghs ◽  
Philipp D. Hafliger
Keyword(s):  
Deep Learning ◽  
Neuronal Cell ◽  
Cell Types ◽  
Learning Approach

scholarly journals Two-Stage Deep Learning Approach to the Classification of Fine-Art Paintings

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 41770-41781 ◽  
Author(s):  
Catherine Sandoval ◽  
Elena Pirogova ◽  
Margaret Lech
Keyword(s):  
Deep Learning ◽  
Fine Art ◽  
Learning Approach ◽  
Two Stage

Classification of productive profile using brain signals: Deep learning and neuroscience meets the mining industry

2021 ◽  
Author(s):  
Ana Siravenha ◽  
Walisson Gomes ◽  
Renan Tourinho ◽  
Sergio Viademonte ◽  
Bruno Gomes
Keyword(s):  
Deep Learning ◽  
Iron Ore ◽  
Signal To Noise Ratio ◽  
Mining Industry ◽  
Eeg Signals ◽  
Neural Data ◽  
Technical Staff ◽  
Brain Signals ◽  
Mining Complex

Classification of electroencephalography (EEG) signals is a complex task. EEG is a non-stationary time process with low signal to noise ratio. Among many methods usedfor EEG classification, those based on Deep Learning (DL) have been relatively successful in providing high classification accuracies. In the present study we aimed at classify resting state EEGs measured from workers of a mining complex. Just after the EEG has been collected, the workers undergonetraining in a 4D virtual reality simulator that emulates the iron ore excavation from which parameters related to their performance were analyzed by the technical staff who classified the workers into four groups based on their productivity. Twoconvolutional neural networks (ConvNets) were then used to classify the workers EEG bases on the same productivity label provided by the technical staff. The neural data was used in three configurations in order to evaluate the amount of datarequired for a high accuracy classification. Isolated, the channel T5 achieved 83% of accuracy, the subtraction of channels P3 and Pz achieved 99% and using all channels simultaneously was 99.40% assertive. This study provides results that add to the recent literature showing that even simple DL architectures are able to handle complex time series such as the EEG. In addition, it pin points an application in industry with vast possibilities of expansion.

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