Quality Assessment of Orange Fruit Images Using Convolutional Neural Networks

2020 ◽  
pp. 403-412
Author(s):  
B. Leelavathy ◽  
Y. S. S. Sri Datta ◽  
Yerram Sai Rachana
Keyword(s):  
Neural Networks ◽  
Quality Assessment ◽  
Convolutional Neural Networks ◽  
Orange Fruit

Automatic quality assessment of apical four-chamber echocardiograms using deep convolutional neural networks

2017 ◽  
Author(s):  
Amir H. Abdi ◽  
Christina Luong ◽  
Teresa Tsang ◽  
Gregory Allan ◽  
Saman Nouranian ◽  
...  
Keyword(s):  
Neural Networks ◽  
Quality Assessment ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks

scholarly journals Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets

Electronics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 45 ◽  
Author(s):  
Chuan-Yu Chang ◽  
Kathiravan Srinivasan ◽  
Wei-Chun Wang ◽  
Ganapathy Pattukandan Ganapathy ◽  
Durai Raj Vincent ◽  
...  
Keyword(s):  
Neural Networks ◽  
Quality Assessment ◽  
Convolutional Neural Networks ◽  
False Positive ◽  
High Performance ◽  
Learning Approaches ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Proposed Model ◽  
Human Eyes

In recent times, the application of enabling technologies such as digital shearography combined with deep learning approaches in the smart quality assessment of tires, which leads to intelligent tire manufacturing practices with automated defects detection. Digital shearography is a prominent approach that can be employed for identifying the defects in tires, usually not visible to human eyes. In this research, the bubble defects in tire shearography images are detected using a unique ensemble hybrid amalgamation of the convolutional neural networks/ConvNets with high-performance Faster Region-based convolutional neural networks. It can be noticed that the routine of region-proposal generation along with object detection is accomplished using the ConvNets. Primarily, the sliding window based ConvNets are utilized in the proposed model for dividing the input shearography images into regions, in order to identify the bubble defects. Subsequently, this is followed by implementing the Faster Region-based ConvNets for identifying the bubble defects in the tire shearography images and further, it also helps to minimize the false-positive ratio (sometimes referred to as the false alarm ratio). Moreover, it is evident from the experimental results that the proposed hybrid model offers a cent percent detection of bubble defects in the tire shearography images. Also, it can be witnessed that the false-positive ratio gets minimized to 18 percent.

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