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Abstract
With the current trend of automation gradually dominating many aspects of human life, the demand for highly accurate and timely responsive automated systems has become essential. Specifically, in the context of transportation, self-driving vehicles, automated traffic monitoring and analysis systems require a capability to read and comprehend the traffic context at a given moment to make informed decisions. My research, "Scene Text Detection for Driving Videos", aims at supporting automated transportation systems in capturing textual information from traffic signs.

• Model:PP-YOLOE+ fromPaddle Detection

PP-YOLOE architecture🡵

• Model:PP-OCRv3 (detection) fromPaddle OCR
  • Using student model for lightweight inference

PP-OCRv3 (detection) architecture🡵

Since Dataset #1 was used in another project with different output, we need to re-process Dataset #1 to match with our project target

• Splitting and filtering images from raw videos
• UsingCVAT to label traffic signs and text
• Label statistics:

Traffic sign classes and data distribution

• Improving M1 performance by combine Dataset #4 into Customized VTSD:
  • Total number of images and number of traffic sign objects increase by ~40%
  • After improved mAP: ~0.69

Improvement sample
(above and below images are before and after improvement, respectively)

#1
#2
#3

• Fine-tuning and combine Scene text recognition module into the system
• Building an End-to-end model based on Transformer
• Developing a web application for demonstration