NAVIGATR: DETECTING AND RECOGNIZING TURKISH TRAFFIC SIGNS USING A NEW DATASET BASED ON DEEP LEARNING

Kasım Özacar

Makale detayları

NAVIGATR: DETECTING AND RECOGNIZING TURKISH TRAFFIC SIGNS USING A NEW DATASET BASED ON DEEP LEARNING

Yazarlar:

Kasım Özacar

Yayıncı:

Karabuk University

Ana Başlıklar:

Deep Learning

Dergi:

Current Trends in Computing Vol.1 Issue 1, pp.44-53

Belge Tipi:

Makale

Yayın Tarihi:

2/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001311311

pISSN:

2980-3152

Anahtar kelimeler:

Özet:

 While cars are becoming smarter than ever with built-in sensing technologies, thanks to the spreading availability of low-cost wearable devices, millions of cars in traffic lack such technologies. However, detecting and recognizing traffic signs is essential in ensuring the safety of pedestrians and drivers. To provide this safety, we conducted a study first to prepare a dataset using collected data in different weather conditions. Then, we used TensorFlows Object Detection API to detect and recognize traffic signs in Turkey. Initially, we collected over 5000 pieces of data for training. We labeled the data in the dataset using a web-based helper application and selected a suitable deep-learning model. After the training process, we evaluated the results of the models and assessed the quality of our prepared dataset. After training the model, we imported it into an Android application that we developed. This application helps navigate drivers by providing information about the signs in front of their cars using text-to-speech technology.

 While cars are becoming smarter than ever with built-in sensing technologies, thanks to the spreading availability of low-cost wearable devices, millions of cars in traffic lack such technologies. However, detecting and recognizing traffic signs is essential in ensuring the safety of pedestrians and drivers. To provide this safety, we conducted a study first to prepare a dataset using collected data in different weather conditions. Then, we used TensorFlows Object Detection API to detect and recognize traffic signs in Turkey. Initially, we collected over 5000 pieces of data for training. We labeled the data in the dataset using a web-based helper application and selected a suitable deep-learning model. After the training process, we evaluated the results of the models and assessed the quality of our prepared dataset. After training the model, we imported it into an Android application that we developed. This application helps navigate drivers by providing information about the signs in front of their cars using text-to-speech technology.

 While cars are becoming smarter than ever with built-in sensing technologies, thanks to the spreading availability of low-cost wearable devices, millions of cars in traffic lack such technologies. However, detecting and recognizing traffic signs is essential in ensuring the safety of pedestrians and drivers. To provide this safety, we conducted a study first to prepare a dataset using collected data in different weather conditions. Then, we used TensorFlows Object Detection API to detect and recognize traffic signs in Turkey. Initially, we collected over 5000 pieces of data for training. We labeled the data in the dataset using a web-based helper application and selected a suitable deep-learning model. After the training process, we evaluated the results of the models and assessed the quality of our prepared dataset. After training the model, we imported it into an Android application that we developed. This application helps navigate drivers by providing information about the signs in front of their cars using text-to-speech technology.

 While cars are becoming smarter than ever with built-in sensing technologies, thanks to the spreading availability of low-cost wearable devices, millions of cars in traffic lack such technologies. However, detecting and recognizing traffic signs is essential in ensuring the safety of pedestrians and drivers. To provide this safety, we conducted a study first to prepare a dataset using collected data in different weather conditions. Then, we used TensorFlows Object Detection API to detect and recognize traffic signs in Turkey. Initially, we collected over 5000 pieces of data for training. We labeled the data in the dataset using a web-based helper application and selected a suitable deep-learning model. After the training process, we evaluated the results of the models and assessed the quality of our prepared dataset. After training the model, we imported it into an Android application that we developed. This application helps navigate drivers by providing information about the signs in front of their cars using text-to-speech technology.

Makale detayları

NAVIGATR: DETECTING AND RECOGNIZING TURKISH TRAFFIC SIGNS USING A NEW DATASET BASED ON DEEP LEARNING

Yazarlar:

Kasım Özacar

Yayıncı:

Karabuk University

Ana Başlıklar:

Deep Learning

Dergi:

Current Trends in Computing Vol.1 Issue 1, pp.44-53

Belge Tipi:

Makale

Yayın Tarihi:

2/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001311311

pISSN:

2980-3152

TensorFlow Object Detection API

TTSD

traffic sign detection.

Sayı

Makale detayları

NAVIGATR: DETECTING AND RECOGNIZING TURKISH TRAFFIC SIGNS USING A NEW DATASET BASED ON DEEP LEARNING

Yazarlar: Kasım Özacar

Yayıncı: Karabuk University

Ana Başlıklar: Deep Learning

Dergi: Current Trends in Computing Vol.1 Issue 1, pp.44-53

Belge Tipi: Makale

Yayın Tarihi: 2/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001311311

pISSN: 2980-3152

TensorFlow Object Detection API

TTSD

traffic sign detection.

Sayı

Yazarlar:

Kasım Özacar

Yayıncı:

Karabuk University

Ana Başlıklar:

Deep Learning

Dergi:

Current Trends in Computing Vol.1 Issue 1, pp.44-53

Belge Tipi:

Makale

Yayın Tarihi:

2/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001311311

pISSN:

2980-3152