scholarly journals Object Detection System Using K-Means Clustering

2021 ◽  
Vol 06 (07) ◽  
Author(s):  
Shreenidhi S ◽  
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Detection System ◽  
Object Classification ◽  
Weight System ◽  
Light Weight ◽  
Clustering Methods

Object Detection is one of the most popular applications in the branch of computer vision. While accuracy has always been the focus, focus has gradually also shifted to lightweight models. In this paper we propose a light weight system where object classification is not required but only object detection using clustering methods.

scholarly journals Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm

2020 ◽  
Vol 28 (S2) ◽  
Author(s):  
Asmida Ismail ◽  
Siti Anom Ahmad ◽  
Azura Che Soh ◽  
Mohd Khair Hassan ◽  
Hazreen Haizi Harith
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Object Classification ◽  
Detection Algorithm ◽  
Learning Object ◽  
Sliding Windows ◽  
Detection Algorithms

The object detection system is a computer technology related to image processing and computer vision that detects instances of semantic objects of a certain class in digital images and videos. The system consists of two main processes, which are classification and detection. Once an object instance has been classified and detected, it is possible to obtain further information, including recognizes the specific instance, track the object over an image sequence and extract further information about the object and the scene. This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. MiniVGGNet is an architecture network used to train an object classification, and the data used for this purpose was collected from specific indoor environment building. For object detection, sliding windows and image pyramids were used to localize and detect objects at different locations, and non-maxima suppression (NMS) was used to obtain the final bounding box to localize the object location. Based on the experiment result, the percentage of classification accuracy of the network is 80% to 90% and the time for the system to detect the object is less than 15sec/frame. Experimental results show that there are reasonable and efficient to combine classic object detection method with a deep learning classification approach. The performance of this method can work in some specific use cases and effectively solving the problem of the inaccurate classification and detection of typical features.

A Technical Assessment on License Plate Detection System

2017 ◽  
pp. 234-258
Author(s):  
Jeena Rita K. S. ◽  
Bini Omman
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Social Life ◽  
Traffic Management ◽  
Intelligent Transportation System ◽  
Detection System ◽  
Human Life ◽  
Transportation Safety ◽  
License Plate ◽  
Active Research

Identifying the region of interest from an image is an important task in the field of computer vision. This process is referred as Object Detection. Locating the object can be done by extracting the features from the image and the features depend on the application. Image retrieval and surveillance are two important applications of Object Detection. Surveillance is an active research topic in computer vision that tries to detect, recognize and track objects over images. An interesting application of it is License Plate Recognition module in Intelligent Transportation System. The speedy developments in economic and social life bring on for a large increase in the number of vehicles in the city. This makes the traffic management difficult. It has great impact on human life as it aims to increase the transportation safety through innovative technologies. An important function that need for majority of ITS application is to identify the vehicle using LPR. This chapter presents assessment on different methods in detecting the license plate and discusses a case study on it.

scholarly journals Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks

2021 ◽  
Vol 11 (24) ◽  
pp. 11868
Author(s):  
José Naranjo-Torres ◽  
Marco Mora ◽  
Claudio Fredes ◽  
Andres Valenzuela
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Visual Analysis ◽  
Detection System ◽  
Detection Algorithm ◽  
Processing Plant ◽  
Human Beings

Raspberries are fruit of great importance for human beings. Their products are segmented by quality. However, estimating raspberry quality is a manual process carried out at the reception of the fruit processing plant,and is thus exposed to factors that could distort the measurement. The agriculture industry has increased the use of deep learning (DL) in computer vision systems. Non-destructive and computer vision equipment and methods are proposed to solve the problem of estimating the quality of raspberries in a tray. To solve the issue of estimating the quality of raspberries in a picking tray, prototype equipment is developed to determine the quality of raspberry trays using computer vision techniques and convolutional neural networks from images captured in the visible RGB spectrum. The Faster R–CNN object-detection algorithm is used, and different pretrained CNN networks are evaluated as a backbone to develop the software for the developed equipment. To avoid imbalance in the dataset, an individual object-detection model is trained and optimized for each detection class. Finally, both hardware and software are effectively integrated. A conceptual test is performed in a real industrial scenario, thus achieving an automatic evaluation of the quality of the raspberry tray, in this way eliminating the intervention of the human expert and eliminating errors involved in visual analysis. Excellent results were obtained in the conceptual test performed, reaching in some cases precision of 100%, reducing the evaluation time per raspberry tray image to 30 s on average, allowing the evaluation of a larger and representative sample of the raspberry batch arriving at the processing plant.

scholarly journals REAL TIME OBJECT DETECTION USING YOLO ALGORITHM

2021 ◽  
Vol 10 (7) ◽  
pp. 61-67
Author(s):  
Bobburi Taralathasri ◽  
Dammati Vidya Sri ◽  
Gadidammalla Narendra Kumar ◽  
Annam Subbarao ◽  
Palli R Krishna Prasad
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Detection ◽  
Detection System ◽  
Image Data ◽  
Image Detection ◽  
The Core ◽  
Wide Range ◽  
Driverless Cars ◽  
Learning Technique

The major and wide range applications like Driverless cars, robots, Image surveillance has become famous in the Computer vision .Computer vision is the core in all those applications which is responsible for the image detection and it became more popular worldwide. Object Detection System using Deep Learning Technique” detects objects efficiently based on YOLO algorithm and applies the algorithm on image data to detect objects.

ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Feature Detection ◽  
Video Stream ◽  
Object Identification ◽  
Vision Systems ◽  
Modern Computer ◽  
Computer Vision Systems ◽  
Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

scholarly journals Transcription Alignment of Historical Vietnamese Manuscripts without Human-Annotated Learning Samples

2021 ◽  
Vol 11 (11) ◽  
pp. 4894
Author(s):  
Anna Scius-Bertrand ◽  
Michael Jungo ◽  
Beat Wolf ◽  
Andreas Fischer ◽  
Marc Bui
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Positive Impact ◽  
Detection System ◽  
Training Data ◽  
Detection Accuracy ◽  
Current State ◽  
Alignment Task ◽  
Scanned Image ◽  
Automatic Transcription

The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task that aims to find a correspondence between text in the scanned image and its existing Unicode counterpart, a correspondence which can then be used as training data. The alignment task can be approached with heuristic methods dedicated to certain types of manuscripts, or with weakly trained systems reducing the required amount of annotations. In this article, we propose a novel learning-based alignment method based on fully convolutional object detection that does not require any human annotation at all. Instead, the object detection system is initially trained on synthetic printed pages using a font and then adapted to the real manuscripts by means of self-training. On a dataset of historical Vietnamese handwriting, we demonstrate the feasibility of annotation-free alignment as well as the positive impact of self-training on the character detection accuracy, reaching a detection accuracy of 96.4% with a YOLOv5m model without using any human annotation.

scholarly journals Rapid Foreign Object Detection System on Seaweed Using VNIR Hyperspectral Imaging

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5279
Author(s):  
Dong-Hoon Kwak ◽  
Guk-Jin Son ◽  
Mi-Kyung Park ◽  
Young-Duk Kim
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Hyperspectral Imaging ◽  
High Speed ◽  
Detection System ◽  
Imaging Techniques ◽  
Foreign Object ◽  
Subtraction Method ◽  
Foreign Objects ◽  
Time Operation

The consumption of seaweed is increasing year by year worldwide. Therefore, the foreign object inspection of seaweed is becoming increasingly important. Seaweed is mixed with various materials such as laver and sargassum fusiforme. So it has various colors even in the same seaweed. In addition, the surface is uneven and greasy, causing diffuse reflections frequently. For these reasons, it is difficult to detect foreign objects in seaweed, so the accuracy of conventional foreign object detectors used in real manufacturing sites is less than 80%. Supporting real-time inspection should also be considered when inspecting foreign objects. Since seaweed requires mass production, rapid inspection is essential. However, hyperspectral imaging techniques are generally not suitable for high-speed inspection. In this study, we overcome this limitation by using dimensionality reduction and using simplified operations. For accuracy improvement, the proposed algorithm is carried out in 2 stages. Firstly, the subtraction method is used to clearly distinguish seaweed and conveyor belts, and also detect some relatively easy to detect foreign objects. Secondly, a standardization inspection is performed based on the result of the subtraction method. During this process, the proposed scheme adopts simplified and burdenless calculations such as subtraction, division, and one-by-one matching, which achieves both accuracy and low latency performance. In the experiment to evaluate the performance, 60 normal seaweeds and 60 seaweeds containing foreign objects were used, and the accuracy of the proposed algorithm is 95%. Finally, by implementing the proposed algorithm as a foreign object detection platform, it was confirmed that real-time operation in rapid inspection was possible, and the possibility of deployment in real manufacturing sites was confirmed.

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