IRJET- OBJECT DETECTION AND CLASSIFICATION USING YOLOV3 (original) (raw)
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IJERT-Object Detection and Classification using YOLOv3
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/object-detection-and-classification-using-yolov3 https://www.ijert.org/research/object-detection-and-classification-using-yolov3-IJERTV10IS020078.pdf Autonomous driving will increasingly require more and more dependable network-based mechanisms, requiring redundant, real-time implementations. Object detection is a growing field of research in the field of computer vision. The ability to identify and classify objects, either in a single scene or in more than one frame, has gained huge importance in a variety of ways, as while operating a vehicle, the operator could even lack attention that could lead to disastrous collisions. In attempt to improve these perceivable problems, the Autonomous Vehicles and ADAS (Advanced Driver Assistance System) have considered to handle the task of identifying and classifying objects, which in turn use deep learning techniques such as the Faster Regional Convoluted Neural Network (F-RCNN), the You Only Look Once Model (YOLO), the Single Shot Detector (SSD) etc. to improve the precision of object detection. YOLO is a powerful technique as it achieves high precision whilst being able to manage in real time. This paper explains the architecture and working of YOLO algorithm for the purpose of detecting and classifying objects, trained on the classes from COCO dataset.
Evaluation of Deep Learning YOLOv3 Algorithm for Object Detection and Classification
Menoufia Journal of Electronic Engineering Research
You Only Look Once version 3 (YOLOv3) is a deep learning model for object detection and classification. It is a single neural network architecture model that uses features from the feeding images and predicts bounding box for all classes of image simultaneously. This paper descript an experimental work for train the deep learning model based on YOLOv3 architecture implemented using Tensor Flow as a deep learning framework. The training process had been done using the data-set PASCAL VOC 2007 and data-set PASCAL VOC 2012 and using The Adaptive Moment Estimation Optimizer (ADM optimizer). The trained model is then tested by using the VOC 2007 test data-set. The final results evaluate the YOLOv3 deep learning model performance for object detection and classification.
Enhancing Object Detection Accuracy Through Custom Dataset Using Yolo
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2024
Potholes pose significant risks to road safety and vehicle maintenance, leading to accidents and costly repairs. Traditional methods of pothole detection are often labour-intensive and time-consuming. In this study, we propose an innovative approach to pothole detection using YOLOv8, a state-of-the-art object detection algorithm. By harnessing the power of deep learning, our system can accurately identify and locate potholes in real-time video streams from traffic cameras and vehicles. We employ YOLOv8, an advanced variant of the You Only Look Once (YOLO) algorithm, known for its speed and accuracy in real-time object detection tasks. Leveraging a large annotated dataset of road images, we fine-tune the YOLOv8 model to specifically detect potholes. Our trained model is capable of identifying various pothole sizes and shapes, even in challenging lighting and weather conditions. The goal of this study is to apply different YOLO models for pothole detection. Three state-of-the-art object detection frameworks (i.e., YOLOv4,YOLOv4-tiny, and YOLOv5s) are experimented to measure their performance involved in real-time responsiveness and detection accuracy using the image set. The image set is identified by running the deep convolutional neural network (CNN) on several deep learning pothole detectors. After collecting a set of 600 images in 720×720 pixels resolution that captures various types of potholes on different road surface conditions, the set is divided into training, testing, and validation subset [1]. I. PROBLEM STATEMENT To implement real-time object detection and recognition in an images captured by webcam and videos in dynamic environment using deep learning model and YOLO ." The primary goal is to detect and recognition Objects in Real-time. We require rich data, all things considered. We need to observe the different type of objects which are moving in respect to the camera. It will help us with perceiving and in recognizing different objects collaboration and interaction. II.
IRJET- Real Time Object Detection Using YOLOv3
IRJET, 2020
Object detection using deep learning has achieved very good performance but there are many problems with images in real-world shooting such as noise, blurring or rotating jitter, etc. These problems have a great impact on object detection. The main objective is to detect objects using You Only Look Once (YOLO) approach. The YOLO method has several advantages as compared to other object detection algorithms. In other algorithms like Convolutional Neural Network (CNN), Fast-Convolutional Neural Network the algorithm will not look at the image completely, but in YOLO ,the algorithm looks the image completely by predicting the bounding boxes using convolutional network and finds class probabilities for these boxes and also detects the image faster as compared to other algorithms. We have used this algorithm for detecting different types of objects and have created an android application which would return voice feedback to the user.
IRJET- Literature Survey on Object Detection using YOLO
IRJET, 2020
Object detection is important for computer vision. The problems such as noise, blurring and rotating jitter, etc. with images in real-world have an important impact on object detection. The objects can be detected in real time using YOLO (You only look once), an algorithm based on convolutional neural networks. This paper addresses the various modifications done to YOLO network which improves the efficiency of object detection.
Real Time Object Detection Using Yolo
IJRASET, 2021
Object detection is related to computer vision and involves identifying the kinds of objects that have been detected. It is challenging to detect and classify objects. Recent advances in deep learning have allowed it to detect objects more accurately. In the past, there were several methods or tools used: R-CNN, Fast-RCNN, Faster-RCNN, YOLO, SSD, etc. This research focuses on "You Only Look Once" (YOLO) as a type of Convolutional Neural Network. Results will be accurate and timely when tested. So, we analysed YOLOv3's work by using Yolo3-tiny to detect both image and video objects.
Real Time Object Detection Using YOLOv3
2020
1,2,3,4 Student, Department of Computer Engineering, TEC, University of Mumbai, Mumbai, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Object detection using deep learning has achieved very good performance but there are many problems with images in real-world shooting such as noise, blurring or rotating jitter, etc. These problems have a great impact on object detection. The main objective is to detect objects using You Only Look Once (YOLO) approach. The YOLO method has several advantages as compared to other object detection algorithms. In other algorithms like Convolutional Neural Network (CNN), Fast-Convolutional Neural Network the algorithm will not look at the image completely, but in YOLO ,the algorithm looks the image completely by predicting the bounding boxes using convolutional network and finds class probabilities for these boxes and also detects the image faster ...
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Vehicle identification and detection is an important part of building intelligent transportation. Various methods have been proposed in this field, but recently the YOLOv8 model has been proven to be one of the most accurate methods applied in various fields. In this study, we propose a YOLOv8 model approach for the identification and detection of 9 vehicle classes in a reprocessed image data set. The steps are carried out by adding labels to the dataset which consists of 2,042 image data for training, 204 validation images and 612 test data. From the results of the training, it produces an accuracy value of 77% with the setting of epoch = 100, batch = 8 and image size of 640. For testing, the YOLOv8 model can detect the type of vehicle on video assets recorded by vehicle activity at intersections with. However, the occlusion problem overlapping vehicle objects has a significant impact on the accuracy value, so it needs to be improved. In addition, the addition of image datasets and...
Object Detection in Specific Traffic Scenes using YOLOv2
ArXiv, 2019
object detection framework plays crucial role in autonomous driving. In this paper, we introduce the real-time object detection framework called You Only Look Once (YOLOv1) and the related improvements of YOLOv2. We further explore the capability of YOLOv2 by implementing its pre-trained model to do the object detecting tasks in some specific traffic scenes. The four artificially designed traffic scenes include single-car, single-person, frontperson-rearcar and frontcar-rearperson.
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.