The accuracy of camera-based object detection (CBOD) built upon deep learning is often evaluated against the real objects in frames only. However, such simplistic evaluation ignores the fact that many unimportant objects are small, distant, or background, and hence, their misdetections have less impact than those for closer, larger, and foreground objects in domains such as autonomous driving. Moreover, sporadic misdetections are irrelevant since confidence on detections is typically averaged across consecutive frames, and detection devices (e.g. cameras, LiDARs) are often redundant, thus providing fault tolerance. This paper exploits such intrinsic fault tolerance of the CBOD process, and assesses in an automotive case study to what extent CBOD can tolerate approximation coming from multiple sources such as lower precision arithmetic, approximate arithmetic units, and even random faults due to, for instance, low voltage operation. We show that the accuracy impact of those sources of approximation is within 1% of the baseline even when considering the three approximate domains simultaneously, and hence, multiple sources of approximation can be exploited to build highly efficient accelerators for CBOD in cars.
翻译:摘要:基于深度学习的相机对象检测(CBOD)的准确性通常只针对帧中的实际对象进行评估。然而,这种简单的评估忽略了许多不重要的对象,因为它们通常比背景中的小、远或背景化,因此,它们的误检测比近距离、较大和前景中的对象更不重要,特别是在自动驾驶等领域。此外,零星的误检测是无关紧要的,因为对检测置信度通常在连续帧中进行平均,并且检测设备(例如相机、激光雷达)通常是冗余的,从而提供了容错性。本文利用CBOD过程的固有容错性,并在汽车案例研究中评估CBOD能够容忍来自多个来源的近似,例如较低精度算术、近似算术单元,甚至是由于低电压操作而导致的随机故障。我们表明,即使考虑到同时考虑三个近似领域,这些近似的准确性影响也在基准线1%以内,并且因此,多个近似源可以被利用来构建汽车中CBOD的高效加速器。