Urban traffic environments present unique challenges for object detection,
particularly with the increasing presence of micromobility vehicles like
e-scooters and bikes. To address this object detection problem, this work
introduces an adapted detection model that combines the accuracy and speed of
single-frame object detection with the richer features offered by video object
detection frameworks. This is done by applying aggregated feature maps from
consecutive frames processed through motion flow to the YOLOX architecture.
This fusion brings a temporal perspective to YOLOX detection abilities,
allowing for a better understanding of urban mobility patterns and
substantially improving detection reliability. Tested on a custom dataset
curated for urban micromobility scenarios, our model showcases substantial
improvement over existing state-of-the-art methods, demonstrating the need to
consider spatio-temporal information for detecting such small and thin objects.
Our approach enhances detection in challenging conditions, including
occlusions, ensuring temporal consistency, and effectively mitigating motion
blur.

通过将连续帧处理的聚合特征映射应用于 YOLOX 架构，结合单帧目标检测的准确性和速度以及视频目标检测框架提供的更丰富特征，引入了一个适应性的检测模型，为解决城市交通环境中的目标检测问题。这种融合为 YOLOX 的检测能力带来了时间视角，提高了对城市移动模式的理解和检测可靠性，从而在检测小型细长目标等具有挑战性的条件下展示出了明显改进。该方法通过考虑时空信息提高了在困难条件下（包括遮挡、保证时序一致性和有效减轻运动模糊等）的检测性能。