Top Research Papers on Object Detection Using YOLO

The first findings of vehicle route accounting reveal that the technique is highly promising, with good outcomes in a number of scenarios, but much more study is needed to make these systems resistant against occlusions and other unforeseen events.

This version eliminates plagiarism while preserving the core ideas of YOLO, a deep learning-based object detection framework designed for real-time applications that achieves high accuracy while maintaining rapid detection capabilities.

The YOLO network is used to train a robust model to improve the average precision (AP) of traffic signs detection in real scenes and the effects of different degradation models on object detection are compared.

The Vehicle Dataset, a comprehensive benchmark for object detection, encompassing seven vehicle classes, including cars, motorcycles, buses, trucks, vans, ambulances, and fire trucks, is presented, underscoring its significance in advancing object detection methodologies.

The YOLO algorithm model detects and recognizes the objects, and the improved model will examine the entire image and split the image into regions and maps the confidence probability using a neural network on the image.

This paper focuses on deep learning and how it is applied to detect and track the objects and popular algorithms of object detection include YOLO, Region-based Convolutional Neural Networks (RCNN), Faster RCNN (F-RCNN).

This paper presents a survey of various detections based on YOLO which aims to improve the accuracy of existing system and presents various modifications done on basic Y OLO method and shows their analysis.

YOLO, a way to deal with item recognition, is demonstrated and Convolutional Neural Network or CNN, is a method which has had the capacity to effectively take care of the picture acknowledgment issue productively.

The main objective of this experiment is to make the computers identify and detect objects in the visual data using YOLO(You Only Look Once) algorithm to detect the objects.

The You Only Look Once (YOLO) V3 technique is suggested to be used for object detection, which results more accuracy of object detection using YOLO algorithm.

Object detection in shelf images can solve many problems in retails sales such as monitoring the number of products on the shelves, completing the missing products and matching the planogram continuously.

The introduction of YOLOF can be an appropriate method to detect objects in documents because it opens up a simple way to exploit image features, making the object detection problem less computationally intensive, but still maintaining the appropriate accuracy.

This research focuses on "You Only Look Once" (YOLO) as a type of Convolutional Neural Network and analysed YOLOv3's work by using Yolo3-tiny to detect both image and video objects.

The CSP residual module in the original network is replaced by the encoding module of transformer to excavate the potential of self attention mechanism in feature expression to improve the original YOLOX combined with transformer.

The evolutionary deep intelligence framework is leveraged to evolve the YOLOv2 network architecture and produce an optimized architecture that has 2.8X fewer parameters with just a ~2% IOU drop, and a motion-adaptive inference method is introduced into the proposed Fast Y OLO framework to reduce the frequency of deep inference with O-YOLO v2 based on temporal motion characteristics.

YOLOv3, a more advanced version of the YOLO algorithm, will be used in this paper to detect objects with greater precision and speed and investigate data augmentation and transfer learning as means of improving the system's performance.

This paper analyzes and summarizes the important basic network, loss function and other improvements in the YOLO series algorithm in detail, and systematically classifies and summarizes the common data sets and performance evaluation indexes of the YOLO series algorithm.

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This work presents a developed application for multiple object detection based on OpenCV libraries that deals with real time systems implementation and gives an indication of where the cases of object detection applications may be more complex and where it may be simpler.

This review covers the evolutionary journey of YOLO from its initial release to the latest versions, encompassing an in-depth analysis of the performance and critical characteristics exhibited by each iteration.