Additional Traffic Sign Detection -- A Comparative Study

Wenzel, Thomas; Brueggert, Steffen; Denzler, Joachim

doi:10.1109/itsc.2015.134

Cited by 4 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Afterward, they classified the shapes with pyramid-HOG features [25]. The author utilized a Maximally Stable Extremal Regions (MSER) based approach to detect the additional panels [26]. In their next study, they found corner areas via aggregated channel features.…”

Section: Additional/supplementary Traffic Panel Detectionmentioning

confidence: 99%

Text-Based Traffic Panels Detection using the Tiny YOLOv3 Algorithm

Kheirinejad¹,

Riahi²,

Azmi³

2022

J. Engg. Res. & Sci.

View full text Add to dashboard Cite

Lately, traffic panel detection has been engrossed by academia and industry. This study proposes a new categorization method for traffic panels. The traffic panels are classified into three classes: symbol-based, text-based, and supplementary/additional traffic panels. Although few types of research have investigated text-based traffic panels, this type is considered in detail in this study. However, there are many challenges in this type of traffic panel, such as having different languages in different countries, their similarity with other text panels, and the lack of suitable quality datasets. The panels need to be detected first to obtain a reasonable accuracy in recognizing the text. Since there are few public text-based traffic panels datasets, this study gathered a novel dataset for the Persian textbased traffic panels all over the streets of Tehran-Iran. This dataset includes two collections of images. The first collection has 9294 images, and the latter has 3305 images. The latter dataset is more monotonous than the first one. Thus, the latter is utilized as the main dataset, and the first is used as an additional dataset. To this end, the algorithm uses the additional dataset for pre-training and the main datasets for training the network. The tiny YOLOv3 algorithm that is fast and has low complexity compared to the YOLOv3 is used for pre-training, training, and testing the data to examine the utility and advantages of the data. The K-fold cross-validation procedure is used to estimate the model's skill on the new data. It achieves 0.973 for Precision, 0.945 for Recall, and 0.955 for Fmeasure.

show abstract

Section: Additional/supplementary Traffic Panel Detectionmentioning

confidence: 99%