论文标题
罗马：运行时对象检测以最大化实时准确性
ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy
论文作者
论文摘要
本文根据分析的发现，分析了动态变化的视频内容和检测潜伏期对检测器的实时检测准确性的影响，并根据分析的发现提出了新的运行时精度变化模型Roma。 Roma旨在实时从一组检测器中选择最佳检测器，而无需标记信息以最大化实时对象检测精度。与由MOT17DET和MOT20DET数据集组成的动态变化的视频内容和检测潜伏期相比，与单个YOLOV4检测器和两种状态的运行时技术相比，在NVIDIA JETSON NANO上使用四个Yolov4检测器的实时准确性提高了4％至37％。
This paper analyzes the effects of dynamically varying video contents and detection latency on the real-time detection accuracy of a detector and proposes a new run-time accuracy variation model, ROMA, based on the findings from the analysis. ROMA is designed to select an optimal detector out of a set of detectors in real time without label information to maximize real-time object detection accuracy. ROMA utilizing four YOLOv4 detectors on an NVIDIA Jetson Nano shows real-time accuracy improvements by 4 to 37% for a scenario of dynamically varying video contents and detection latency consisting of MOT17Det and MOT20Det datasets, compared to individual YOLOv4 detectors and two state-of-the-art runtime techniques.
