2024 Volume 5 Issue 3 Pages 418-426
In our country, AI-based survey methods are being promoted to streamline traffic volume surveys. Existing research has shown that vehicle section identification using AI can count sectional traffic volumes by vehicle type, but accuracy decreases due to flares and scenery reflections. Additionally, vehicle types can be determined by recognizing the leading number of the classification code on license plates, but the recognition accuracy decreases when the characters are unclear. Therefore, this study proposes a method to determine vehicle types by recognizing classification codes when the characters are clear and by using vehicle section identification results when the characters are unclear. Applying the proposed method to videos taken at three different locations resulted in a high accuracy with an F-measure of 0.95 or higher at all locations. In the future, we aim for early practical application in automotive traffic volume surveys by accommodating license plates with designs.
