Training Process Automation for Computer Vision (original) (raw)

IJERT-Automation using Machine Learning and Object Detection

International Journal of Engineering Research and Technology (IJERT), 2021

https://www.ijert.org/automation-using-machine-learning-and-object-detection https://www.ijert.org/research/automation-using-machine-learning-and-object-detection-IJERTCONV9IS03047.pdf A major challenge in many of the object detection systems is the dependency on other computer vision techniques for helping the deep learning-based approach, which leads to slow and non-optimal performance. In this paper, a completely deep learning-based approach is used to solve the problem of object detection in an end-to-end fashion. The paper aims to incorporate state-of-the-art technique for detecting the object placed in front of the webcam with the goal of achieving high accuracy with a real-time performance using deep learning. Based on the detected image several preprogrammed robots are used to transport the object in the detected image from the place where humans cannot work flawlessly to the desired location efficiently. This paper comes with the combination of deep learning and robotics which can be used in several areas such as mines, construction sites, steel factories etc where human works in a risky environment. The network is trained on the most publicly available data set, on which an object detection challenge is conducted annually.

Analysis of Machine Learning Approach for Object Detection

International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020

Object detection is one of the main and most difficult computer vision branches widely used to track instances of semantically objects of a certain class in the lives of individuals, such as security monitoring, autonomous drive, etc. The efficiency of object detectors has been significantly enhanced with the rapid growth of deep learning networks for detection tasks. The architecture suggested uses pre-trained networks such as ALEXNET and VGG-16 to identify specific artifacts utilizing a PASCAL VOC 2007 dataset in today's language. The 25 layers of ALEXNET and VGG-16 are 41. Two principal directions are explored: supervised learning and semi-monitored learning. The disadvantages of supervised learning approaches drive unattended pre-training to be explored. By studying strong representations in early layers, layers can be educated quicker and more effectively.

A System for Rapid Interactive Training of Object Detectors

Lecture Notes in Computer Science, 2008

Machine learning approaches have become the de-facto standard for creating object detectors (such as face and pedestrian detectors) which are robust to lighting, viewpoint, and pose. Generating su ciently large labeled data sets to support accurate training is often the most challenging problem. To address this, the active learning paradigm suggests interactive user input, creating an initial classifier based on a few samples and refining that classifier by identifying errors and retraining. In this paper we seek to maximize the e ciency of the user input; minimizing the number of labels the user must provide and minimizing the accuracy with which the user must identify the object. We propose, implement, and test a system that allows an untrained user to create high-quality classifiers in minutes for many diā†µerent types of objects in arbitrary scenes.

Approach to Automated Visual Inspection of Objects Based on Artificial Intelligence

Applied Sciences, 2022

The article discusses the possibility of object detector usage in field of automated visual inspection for objects with specific parameters, specifically various types of defects occurring on the surface of a car tire. Due to the insufficient amount of input data, as well as the need to speed up the development process, the Transfer Learning principle was applied in a designed system. In this approach, the already pre-trained convolutional neural network AlexNet was used, subsequently modified in its last three layers, and again trained on a smaller sample of our own data. The detector used in the designed camera inspection system with the above architecture allowed us to achieve the accuracy and versatility needed to detect elements (defects) whose shape, dimensions and location change with each occurrence. The design of a test facility with the application of a 12-megapixel monochrome camera over the rotational table is briefly described, whose task is to ensure optimal conditions...

Real time object recognition for teaching neural networks

1999

Undergraduate students in computer science learn best when they are given the opportunity to apply hardware and software concepts to real world systems, and neural-network applications present attractive possibilities for giving them such opportunity. An example of how to take advantage of these possibilities is given in this paper, which describes a specific neural network technique that has been developed and applied to the problem of identifying real world objects in real time.

Image Processing in Artificial Intelligence

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020

Machines can learn to elucidate images the same way our brains do and analyse those images much more thoroughly than we can. When applied to Image Processing, Artificial Intelligence (AI) can propel face recognition and security functionality in public places, detecting and recognizing intruders, objects, and patterns in live images and videos, etc. Image processing technology focuses on the development of data extraction methods applied to the statistical classification of visual imagery. In classical image processing systems, an image is pre-processed to remove noise (denoising), segmented to produce close object boundaries, analysed to extract a representative feature, and compared to the ideal object feature vectors by a classifier to decide the nearest object classification and its associated level. In this paper, we discuss about digital image processing and the role of AI in it.

Computer Vision Based Automation System for Detecting Objects

2015

Software Quality Assurance Testing time computer vision based automation tools are used to test the window based application and window based application is combined of many objects. Among them most of the automation tool detect window objects by comparing images. Most of the objects are visible in the window screen but some objects which are not visible to the screen at the first time. Proper interaction with the window application hidden objects get visible to the screen like dropdown list item, editor text object, list box item and slider. With the automation tools these hidden objects cannot be searched directly. In this paper proposes some methods which will enhance the automation tools to access the window application hidden objects. overcome these two limitations and works for complete introducing full testing framework for hidden object detection.

Supporting artificial intelligence with artificial images

2018

Infrared (IR) imagery is frequently used in security/surveillance and military image processing applications. In this article we will consider the problem of outlining military naval vessels in such images. Obtaining these outlines is important for a number of applications, for instance in vessel classification. Detecting this outline is basically a very complex image segmentation task. We will use a special neural network for this purpose. Neural networks have recently shown great promise in a wide range of image processing applications, image segmentation is no exception in this regard. The main drawback when using neural networks for this purpose is the need for substantial amounts of data in order to train the networks. This problem is of particular concern for our application due to the difficulty in obtaining IR images of military vessels. In order to alleviate this problem we have experimented with using alternatives to true IR images for the training of the neural networks. ...

Application of Machine Learning in Computer Vision: A Brief Review

International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020

A scientific study on the importance of machine learning and its applications in the field of computer vision is carried out in this paper. Recent advancements in Artificial Intelligence, deep learning, computing resources and availability of large training datasets made tasks such as computer vision and natural language processing extremely fast and accurate. Thus Artificial intelligence is a trending topic in the field of computing. Deep learning is a subcategory of machine learning in the field of artificial intelligence. Image processing task can be performed efficiently by using machine learning methods, thus machine learning will provide a better understanding of complex images. Object detection, recognition and tracking are the fields related to computer vision. In the computer vision with the help convolutional neural network-based algorithms like YOLO and R-CNN make a big leap in this field. Algorithms based on machine learning models are excellent at recognizing patterns but typically requires an enormous amount of data sets and lots of computational power. Generally, the neural network requires graphics processing unit for faster execution of machine learning models. This review paper gives a brief overview of real-time object detection and machine learning algorithms implemented by various researchers around the world. Also, this paper consists of a study of various methodology used to detect and recognize a particular object in the image. Real-time object detection algorithms are going to play a vital role in the field of computer vision.