@misc{Piotr_GÓRAL_Research_2025-06-30,
 author={Piotr GÓRAL and Piotr GÓRAL},
 copyright={Wojskowa Akademia Techniczna},
 address={Warszawa},
 address={Warszawa},
 howpublished={online},
 year={2025-06-30},
 year={2025-06-30},
 publisher={Wojskowa Akademia Techniczna},
 publisher={Wojskowa Akademia Techniczna},
 language={angielski},
 language={angielski},
 abstract={This article presents research concerning the recognition of road traffic lights. Initially, vision algorithms were analysed regarding their suitability for implementation in vehicle control systems dedicated to individuals with specific communication needs. The paper presents the results of experimental studies on a vision system for recognising traffic lights, conducted using convolutional neural networks (CNNs). For the experiment, a custom database of traffic light images was prepared. This database was utilised to train a selected Xception CNN model and for processing by a classic algorithm based on colour analysis in the HSV colour space. The obtained classification accuracy results, reaching 98.75%, could serve as a 'green light' for implementing the developed technology to assist driving. The research findings may also find application in driver assistance systems, with particular attention given to the mobility of people with specific needs, such as those with visual impairments.},
 abstract={This article presents research concerning the recognition of road traffic lights. Initially, vision algorithms were analysed regarding their suitability for implementation in vehicle control systems dedicated to individuals with specific communication needs. The paper presents the results of experimental studies on a vision system for recognising traffic lights, conducted using convolutional neural networks (CNNs). For the experiment, a custom database of traffic light images was prepared. This database was utilised to train a selected Xception CNN model and for processing by a classic algorithm based on colour analysis in the HSV colour space. The obtained classification accuracy results, reaching 98.75%, could serve as a 'green light' for implementing the developed technology to assist driving. The research findings may also find application in driver assistance systems, with particular attention given to the mobility of people with specific needs, such as those with visual impairments.},
 title={Research on the Accuracy of Automatic Vision Algorithms for Classifying Traffic Lights},
 title={Research on the Accuracy of Automatic Vision Algorithms for Classifying Traffic Lights},
 type={artykuł},
 type={artykuł},
 keywords={Drogowa sygnalizacja światlna, Drogowa sygnalizacja światlna},
}