Alapfogalmak
The authors introduce a novel detection model that combines single-frame precision with video object detection capabilities to accurately identify micromobility vehicles in urban environments.
Kivonat
The content discusses the challenges of detecting micromobility vehicles in urban traffic scenarios and introduces a new detection model, FGFA-YOLOX, that leverages both single-frame and video-based object detection methodologies. The model aims to enhance detection consistency and accuracy by incorporating spatio-temporal information. It was tested on a custom dataset curated for micromobility scenarios, showcasing substantial improvement over existing state-of-the-art methods. The paper also compares the performance of the proposed model with other SOTA object detectors, highlighting its superior performance in terms of mAP and mAP@50 metrics. Additionally, the content provides insights into the experimental setup, evaluation metrics, implementation details, and model training process.
Statisztikák
Our proposed FGFA-YOLOX model achieves an mAP of 38.6%.
Inference time per frame for FGFA-RFCN is 418.1 milliseconds.
YOLOv8 has an mAP@50 score of 64.2%.
Idézetek
"Our approach enhances detection in challenging conditions, including occlusions."
"Our method can benefit from the large number of readily available pre-trained models for YOLOX."
"The combined strengths of our model offer a significant advancement in addressing the diverse challenges of urban traffic conditions."