International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
www.irjet.net
Object Detection and Tracking AI Robot Jayati Bhardwaj1, Mitali2, Manu Verma3, Madhav4 1,2,3,4 Department of CSE MIT, Moradabad, U.P, India
---------------------------------------------------------------------------***-------------------------------------------------------------------------In this paper, we present a comprehensive examination of Abstract: The task of object detection is essential in the
the state-of-the-art in object detection utilizing AI robots. We concentrate on recent advances in the area, discussing both conventional and deep learning-based approaches and highlighting their advantages and disadvantages [15]. We also go over numerous robotics uses for object detection, like grasping, manipulating, and navigation, and we give some insight into the difficulties and potential future directions of this field of study.
fields of robotics and computer vision. This paper's goal is to provide an overview of recent developments in object detection utilizing AI robots. The study explores several object detection techniques, including deep learning-based methods and their drawbacks. The study also gives a general overview of how object detection is used in practical contexts like robots and self-driving cars. The advantages of deploying AI robots for object detection over conventional computer vision techniques are the main topics of discussion.
The goals of this paper are to offer an overview of the subject's current state and to encourage more research and growth in it
I. INTRODUCTION
II. LITERATURE REVIEW
Artificial intelligence (AI) and its uses in robotics have attracted increasing attention in recent years. Providing robots with the ability to detect and recognize items in their environment is one of the key issues in robotics. As the name suggests, object detection is the procedure of locating items in an image or video frame and creating bounding boxes around them[3]. For robots to carry out a variety of activities, including grabbing, manipulating, and navigating, this task is essential [7].
For many years, object identification algorithms have been being developed for AI robots. Early object detection techniques relied on manually made features, such as scale-invariant feature transforms, histograms of directed gradients, and sped-up robust features (SURF) (HOG)[6]. These methods have been widely applied to AI robots for object detection; however they have a number of shortcomings. For example, they are sensitive to the size and orientation of objects and have trouble capturing intricate shapes and textures.
Traditionally, hand-crafted features like SIFT and HOG and machine learning methods like SVM and Random Forest have been used for object detection. However, these techniques have limits in terms of effectiveness and precision, particularly Growing interest has been shown in artificial intelligence (AI) and its uses in robotics in recent years [5]. Giving robots the ability to detect and recognize items in their environment is one of the most significant robotics challenges. The act of recognizing objects in an image or video frame and creating bounding boxes around them is known as object detection, as the name suggests. Robots must complete this activity in order to carry out other activities including grasping, manipulating, and navigating [9].
The use of deep learning-based object detection techniques has increased recently[9]. These convolutional neural network (CNN)-based techniques have been shown to outperform more traditional feature-based methods in a variety of object identification tasks. The three most prevalent deep learning-based object detection techniques are region-based convolutional neural networks, You Only Look Once (YOLO), and single shot multi-box detectors (SSD) (R-CNN).These techniques may automatically learn object features and carry out object detection at the same time since they are trained from beginning to end.
Traditionally, hand-crafted features and machine learning algorithms like SVM and Random Forest have been used for object detection [14]. Examples of these features include SIFT and HOG. However, these techniques have shortcomings, particularly in terms of efficiency and accuracy.
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Dealing with occlusions, where objects are partially or fully obscured from vision, is one of the difficulties in object detection. Several approaches have been put forth to deal with this problem, such as attention-based approaches, where the AI robot focuses on the portions of the image that are the most pertinent, and occlusion-
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