Computer vision tasks such as object detection are crucial for the operations of autonomous vehicles (AVs). Results of many tasks, even those requiring high computational power, can be obtained within a short delay by offloading them to edge clouds. However, although edge clouds are exploited, real-time object detection cannot always be guaranteed due to dynamic channel quality. To mitigate this problem, we propose an edge-network-assisted real-time object detection framework (EODF). In an EODF, AVs extract the region of interest (Rols) of the captured image when the channel quality is not sufficiently good for supporting real-time object detection. Then AVs compress the image data on the basis of the Rols and transmit the compressed one to the edge cloud. In so doing, real-time object detection can be achieved due to the reduced transmission latency. To verify the feasibility of our framework, we evaluate the probability that the results of object detection are not received within the inter-frame duration (i.e., outage probability) and their accuracy. From the evaluation, we demonstrate that the proposed EODF provides the results to AVs in real time and achieves satisfactory accuracy.

中文翻译：

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用于自动驾驶的边缘网络辅助实时目标检测框架


物体检测等计算机视觉任务对于自动驾驶车辆 (AV) 的操作至关重要。许多任务的结果，甚至是那些需要高计算能力的任务，都可以通过将其卸载到边缘云而在短时间内获得。然而，尽管利用了边缘云，但由于动态信道质量，无法始终保证实时目标检测。为了缓解这个问题，我们提出了一种边缘网络辅助的实时对象检测框架（EODF）。在 EODF 中，当通道质量不足以支持实时对象检测时，AV 会提取捕获图像的感兴趣区域 (Rols)。然后AV根据Rols压缩图像数据并将压缩后的图像数据传输到边缘云。这样做，由于减少了传输延迟，可以实现实时对象检测。为了验证我们框架的可行性，我们评估了在帧间持续时间内未收到对象检测结果的概率（即中断概率）及其准确性。通过评估，我们证明所提出的 EODF 可以实时向 AV 提供结果，并达到令人满意的精度。