CCTSDB dataset enhancement based on a cross-augmentation method for image datasets

图像(数学) 计算机科学 人工智能 模式识别(心理学)
作者
Xiaodong Lin,Wei Wang,Xijing Zhu,Yong Yue
出处
期刊:Intelligent Data Analysis [IOS Press]
卷期号:: 1-19
标识
DOI:10.3233/ida-230075
摘要

In the digital era, the rapid advancement of artificial intelligence has put a spotlight on target detection, especially in traffic settings. This area of study is pivotal for crucial projects like autonomous vehicles, road monitoring, and traffic sign recognition. However, existing Chinese traffic datasets lack comprehensive benchmarks for traffic signs and signals, and foreign datasets do not match Chinese traffic conditions. Manually annotating a large-scale dataset tailored for Chinese traffic conditions presents a significant challenge. This study addresses this gap by proposing a cross-augmentation method for image datasets. We utilized YOLOX for target detection and trained models on the BDD100K dataset, achieving an impressive mAP of 60.25%, surpassing most algorithms. Leveraging transfer learning, we enhanced the CCTSDB dataset, creating the ACCTSDB dataset, which includes annotations for common traffic objects and Chinese traffic signs. Using YOLOX, we trained a traffic detector tailored for Chinese traffic scenarios, achieving an mAP of 75.79%. To further validate our approach, we conducted experiments on the TT100K dataset and successfully introduced the ATT100K dataset. Our methodology is poised to alleviate the limitations of manually annotating image datasets. The proposed ACCTSDB dataset and ATT100K dataset are expected to compensate for the lack of large-scale, multi-class traffic datasets in China.

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