Comparison of Stock Price Prediction Models using Pre-trained Neural Networks
Volume-3 | Issue-2
Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach
Volume-3 | Issue-2
Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division
Volume-3 | Issue-2
Blockchain Framework for Communication between Vehicle through IoT Devices and Sensors
Volume-3 | Issue-2
Gas Leakage Detection in Pipeline by SVM classifier with Automatic Eddy Current based Defect Recognition Method
Volume-3 | Issue-3
A Review on Data Securing Techniques using Internet of Medical Things
Volume-3 | Issue-3
Machine Learning Algorithms Performance Analysis for VLSI IC Design
Volume-3 | Issue-2
Maximizing the Prediction Accuracy in Tweet Sentiment Extraction using Tensor Flow based Deep Neural Networks
Volume-3 | Issue-2
Assimilation of IoT sensors for Data Visualization in a Smart Campus Environment
Volume-3 | Issue-4
DDOS ATTACK DETECTION IN TELECOMMUNICATION NETWORK USING MACHINE LEARNING
Volume-1 | Issue-1
Gas Leakage Detection in Pipeline by SVM classifier with Automatic Eddy Current based Defect Recognition Method
Volume-3 | Issue-3
Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division
Volume-3 | Issue-2
Comparison of Stock Price Prediction Models using Pre-trained Neural Networks
Volume-3 | Issue-2
Construction of a Framework for Selecting an Effective Learning Procedure in the School-Level Sector of Online Teaching Informatics
Volume-3 | Issue-4
Machine Learning Algorithms Performance Analysis for VLSI IC Design
Volume-3 | Issue-2
Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach
Volume-3 | Issue-2
Characterizing WDT subsystem of a Wi-Fi controller in an Automobile based on MIPS32 CPU platform across PVT
Volume-2 | Issue-4
Assimilation of IoT sensors for Data Visualization in a Smart Campus Environment
Volume-3 | Issue-4
Design of Data Mining Techniques for Online Blood Bank Management by CNN Model
Volume-3 | Issue-3
Ethereum and IOTA based Battery Management System with Internet of Vehicles
Volume-3 | Issue-3
Volume - 5 | Issue - 2 | june 2023
Published
27 June, 2023
Vehicle detection in degraded hazy conditions poses significant challenges in computer vision. It is difficult to detect objects accurately under hazy conditions because vision is reduced, and color and texture information is distorted. This research paper presents a comparative analysis of different YOLO (You Only Look Once) methodologies, including YOLOv5, YOLOv6, and YOLOv7, for object detection in mixed traffic under degraded hazy conditions. The accuracy of object detection algorithms can be significantly impacted by hazy weather, so creating reliable models is critical. An open-source dataset of footage obtained from security cameras installed on traffic signals is used for this study to evaluate the performance of these algorithms. The dataset includes various traffic objects under varying haze levels, providing a diverse range of atmospheric conditions encountered in real-world scenarios. The experiments illustrate that the YOLO-based techniques are effective at detecting objects in degraded hazy conditions and give information about how well they perform in comparison. The findings help object detection models operate more accurately and consistently under adverse weather conditions.
KeywordsObject detection Degraded hazy conditions YOLO Vehicle detection
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