AI Modeling for Long-Distance Traffic Sign Detection Using Small Object Detection Data

Object detection, a key area in computer vision, involves locating and classifying objects within images. The YOLO model, which combines candidate region selection, coordinate prediction, and class inference into a single stage, has set a standard for this field. Since its introduction, numerous models have been developed, with applications spanning both academia and industry. Advances in computer vision AI, sensor, and computing technologies have been foundational for autonomous driving and driver assistance systems. Specifically, traffic sign recognition systems using object detection algorithms enhance driving safety. Annotating data for these models is resource-intensive, so many rely on benchmark datasets like MS COCO. However, this dataset has limited images featuring small objects, leading to a gap in research on models capable of detecting significantly smaller objects. Our study addresses this by developing an object detection model for distant road signs as small objects, using a specialized dataset with 2,800 x 2,100 resolution images containing objects smaller than 200 x 200 pixels. We empirically evaluated various models, including State-of-the-art YOLO version and Transformer-based approaches, under different training conditions. The best-performing model for small object detection, balancing accuracy and inference speed, was the pretrained YOLO v6m, utilizing transfer learning with COCO2017 weights without self-distillation. This research contributes to small object detection technology, offering insights for developing new AI algorithms and benefiting both academic and industrial sectors.