Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm (original) (raw)
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Object Detection using Deep Learning Algorithm CNN
International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020
Object Detection is an emerging technology in the field of Computer Vision and Image Processing that deals with detecting objects of a particular class in digital images. It has considered being one of the complicated and challenging tasks in computer vision. Earlier several machine learning-based approaches like SIFT (Scale-invariant feature transform) and HOG (Histogram of oriented gradients) are widely used to classify objects in an image. These approaches use the Support vector machine for classification. The biggest challenges with these approaches are that they are computationally intensive for use in real-time applications, and these methods do not work well with massive datasets. To overcome these challenges, we implemented a Deep Learning based approach Convolutional Neural Network (CNN) in this paper. The Proposed approach provides accurate results in detecting objects in an image by the area of object highlighted in a Bounding Box along with its accuracy.
Object Detection using Deep Learning Approach
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
The most often utilized strategies for current deep learning models to accomplish a multitude of activities on devices are mobile networks and binary neural networks. In this research, we propose a method for identifying an object using the pretrained deep learning model MobileNet for Single Shot Multi-Box Detector (SSD). This technique is utilized for real-time detection as well as webcams to detect the object in a video feed.To construct the module, we use the MobileNet and SSD frameworks to provide a faster and effective deep learning-based object detection approach.Deep learning has evolved into a powerful machine learning technology that incorporates multiple layers of features or representations of data to get cuttingedge results. Deep learning has demonstrated outstanding performance in a variety of fields, including picture classification, segmentation, and object detection. Deep learning approaches have recently made significant progress in fine-grained picture categorization, which tries to differentiate subordinate-level categories.The major goal of our study is to investigate the accuracy of an object identification method called SSD, as well as the significance of a pre-trained deep learning model called MobileNet.To perform this challenge of detecting an item in an image or video, I used OpenCV libraries, Python, and NumPy. This enhances the accuracy of behavior recognition at a processing speed required for real-time detection and daily monitoring requirements indoors and outdoors.
Due to its numerous applications and new technological advancements, object detection has gained more attention in the last few years. This study examined various uses of some deep learning object detection algorithms. These algorithms are divided into two-stage detectors like Region Based Convolutional Neural Network (R-CNN), Fast Region Based Convolutional Neural Network (Faster R-CNN), and Faster Region Based Convolutional Neural Network (Faster R-CNN), and one-stage detectors like Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) algorithms that are used in text and face detection, image retrieval, security, surveillance, traffic control, traffic sign/light detection, pedestrian detection and in medical areas among others. This research primarily focuses on three applications: drone surveillance, applications relating to traffic, and medical fields. Findings from the performed analysis indicate that YOLO stands out as the predominant algorithm for drone surveillance among different deep learning models used in various application fields and being a one-stage detector. In terms of usage in traffic-related applications, SSD proved to be a prominent one-stage detector alongside Faster R-CNN which gained popularity as a two-stage detector preferred for applications in the medical field.
IMAGE DETECTION USING DEEP LEARNING
ijetrm journal , 2022
Image Detection is the branch of the Technology of information and software Systems which can recognize as well as understand images and scenes. Image detection consists of various aspects such as image recognition, image generation, image super-resolution and many more. Object detection is widely used for face detection, vehicle detection, pedestrian counting, web images, security systems and self-driving cars in this project, we are using highly accurate object detection-algorithms and CNN. Using these methods and algorithms, based on deep learning which is also based on machine learning require lots of mathematical and deep learning frameworks understanding by using dependencies such as Tensor Flow, Open CV, Image, Al etc. We can detect each and every object in image by the area object in a highlighted rectangular box and identify each and every object and assign its tag to the object. Image or object detection is a computer technology that processes the image and detects objects in it. We discuss the methods and approaches utilized to detect objects in this study.
Science Insights, 2019
Traditional image classification methods are difficult to process huge image data and cannot meet people's requirements for image classification accuracy and speed. Convolutional neural networks have achieved a series of breakthrough research results in image classification, object detection, and image semantic segmentation. This method broke through the bottleneck of traditional image classification methods and became the mainstream algorithm for image classification. Its powerful feature learning and classification capabilities have attracted widespread attention. How to effectively use convolutional neural networks to classify images have become research hotspots. In this paper, after a systematic study of convolutional neural networks and an in-depth study of the application of convolutional neural networks in image processing, the mainstream structural models, advantages and disadvantages, time / space used in image classification based on convolutional neural networks are given. Complexity, problems that may be encountered during model training, and corresponding solutions. At the same time, the generative adversarial network and capsule network based on the deep learning-based image classification extension model are also introduced; simulation experiments verify the image classification In terms of accuracy, the image classification method based on convolutional neural networks is superior to traditional image classification methods. At the same time, the performance differences between the currently popular convolutional neural network models are comprehensively compared and the advantages and disadvantages of various models are further verified. Experiments and analysis of overfitting problem, data set construction method, generative adversarial network and capsule network performance.■
Object Detection Using Deep Learning
2020
It has received much research attention in recent years because of the close relationship between object detection and video analysis and image comprehension. Traditional methods of object detection are based on hand-crafted features and architectures that are flawlessly trainable. Their performance stagnates easily by constructing complex ensembles that combine multiple lowlevel image features with high-level context from object detectors and scene classifiers. With the rapid growth of deep learning, more efficient techniques are implemented to solve the problems inherent in conventional architectures, capable of learning semantic, high-level, and deeper features. These models act differently in the context of network design, training strategy, and optimization.
REVIEW ON OBJECT DETECTION WITH CNN
IRJET, 2022
Object detection is one of the most important achievements of deep learning and image processing since it finds and recognises objects in images. An object detection model may be trained to recognise and detect several objects, making it adaptable. Object detection had a large scope of interest even before deep learning approaches and modern-day image processing capabilities. It has become considerably more widespread in the present generation, thanks to the development of convolutional neural networks (CNNs) and the adaptation of computer vision technology. The latest wave of deep learning approaches to object detection gives up apparently limitless possibilities.
Object Detection and Data Classification with Deeplearning Model Using Tensorflow
2020
Deep learning is the sub set of machine learning in artificial intelligence. The major role is played in deep learning is analysis the inner function of the object and make effective decision. Deep learning achieves higher success results in many applications and can provide faster results in data analytics process. Object has more number of patterns and geographical movements. Analysing these pattern we need effecting classification method with maximum efficiency and minimum processing time. In this paper, we used TensorFlow for detecting object from real time video stream data. This neural network model and create data pattern for each objects. TensorFLow MNIST dataset is used data classficiation and analytics process. This library files are developed my Google. The multiple actions and classification techniques are analysed. The experimental results are analysed with liner unit, softpuls, softsign and convolution neural network. The result shows that more accurate and objects det...
A Review of Object Detection Models Based on Convolutional Neural Network
Advances in Intelligent Systems and Computing, 2020
Convolutional Neural Network (CNN) has become the stateof-the-art for object detection in image task. In this chapter, we have explained different state-of-the-art CNN based object detection models. We have made this review with categorization those detection models according to two different approaches: two-stage approach and one-stage approach. Through this chapter, it has shown advancements in object detection models from R-CNN to latest RefineDet. It has also discussed the model description and training details of each model. Here, we have also drawn a comparison among those models.
Object Detection Techniques based on Deep Learning: A Review
Computer Science & Engineering: An International Journal, 2022
Object detection is a computer technique that searches digital images and videos for occurrences of meaningful subjects in particular categories (such as people, buildings, and automobiles). It is related to computer vision and image processing. Two well-studied aspects of identification are facial and pedestrian detection. Object detection is useful in a wide range of visual recognition tasks, including image retrieval and video monitoring. The object detection algorithm has been improved many times to improve the performance in terms of speed and accuracy. “Due to the tireless efforts of many researchers, deep learning algorithms are rapidly improving their object detection performance. Pedestrian detection, medical imaging, robotics, self-driving cars, face recognition and other popular applications have reduced labor in many areas.” It is used in a wide variety of industries, with applications range from individual safeguarding to business productivity. It is a fundamental compo...