Varietal quality control in the nursery plant industry using computer vision and deep learning techniques

Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles. Deep learning is growing algorithm that gains a big success in computer vision problem. Currently, many research communities have proposed deep learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN. Notably, this article describes how deep learning makes the OSN security technique more intelligent for detecting malicious activity by establishing a classifier model.

Computer Vision with Deep Learning Techniques for Neurodegenerative Diseases Analysis Using Neuroimaging: A Survey

10.1007/978-981-16-2597-8_15 ◽

2021 ◽

pp. 179-189

Author(s):

Richa Vij ◽

Sakshi Arora

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Neurodegenerative Diseases ◽

Learning Techniques

Underwater Gesture Recognition Using Classical Computer Vision and Deep Learning Techniques

Journal of Image and Graphics ◽

10.18178/joig.8.1.9-14 ◽

2020 ◽

Vol 8 (1) ◽

pp. 9-14

Author(s):

Mygel Andrei M. Martija ◽

Jakov Ivan S. Dumbrique ◽

Prospero C., Jr Naval

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Gesture Recognition ◽

Learning Techniques ◽

Classical Computer

Segmentation for Complex Background Images using Deep Learning Techniques

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1042.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 1746-1750

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Learning Algorithm ◽

Vision System ◽

Automated Segmentation ◽

Computer Vision System ◽

Important Stage ◽

Deep Learning Algorithm ◽

Complex Background ◽

Learning Techniques

Segmentation is an important stage in any computer vision system. Segmentation involves discarding the objects which are not of our interest and extracting only the object of our interest. Automated segmentation has become very difficult when we have complex background and other challenges like illumination, occlusion etc. In this project we are designing an automated segmentation system using deep learning algorithm to segment images with complex background.

Deep learning for the quality control of thermoforming food packages

Scientific Reports ◽

10.1038/s41598-021-01254-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Núria Banús ◽

Imma Boada ◽

Pau Xiberta ◽

Pol Toldrà ◽

Narcís Bustins

Keyword(s):

Computer Vision ◽

Quality Control ◽

Deep Learning ◽

Production Line ◽

Modular Design ◽

Vision System ◽

False Negative ◽

Control Process ◽

Food Packages ◽

Quality Control Process

AbstractQuality control is a key process designed to ensure that only products satisfying the defined quality requirements reach the end consumer or the next step in a production line. In the food industry, in the packaging step, there are many products that are still evaluated by human operators. To automate the process and improve efficiency and effectiveness, computer vision and artificial intelligence techniques can be applied. This automation is challenging since specific strategies designed according to the application scenario are required. Focusing on the quality control of the sealing and closure of matrix-shaped thermoforming food packages, the aim of the article is to propose a deep-learning-based solution designed to automatically perform the quality control while satisfying production cadence and ensuring 100% inline inspection of the products. Particularly, the designed computer vision system and the image-based criteria defined to determine when a product has to be accepted or rejected are presented. In addition, the vision control software is described with special emphasis on the different convolutional neural network (CNN) architectures that have been considered (ResNet18, ResNet50, Vgg19 and DenseNet161, non-pre-trained and pre-trained on ImageNet) and on the specifically designed dataset. To test the solution, different experiments are carried out in the laboratory and also in a real scenario, concluding that the proposed CNN-based approach improves the efficiency and security of the quality control process. Optimal results are obtained with the pre-trained DenseNet161, achieving false positive rates that range from 0.03 to 0.30% and false negative rates that range from 0 to 0.07%, with a rejection rate between 0.64 and 5.09% of production, and being able to detect at least 99.93% of the sealing defects that occur in any production. The modular design of our solution as well as the provided description allow it to adapt to similar scenarios and to new deep-learning models to prevent the arrival of faulty products to end consumers by removing them from the automated production line.