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：基于深度学习的摄像头目标检测（CBOD）的准确性通常仅通过与帧中真实物体的对比来评估。然而，这种简单化的评估忽略了在自动驾驶等领域中许多不重要物体（如小型、远处或背景物体）的漏检相比近距离、大型及前景物体影响更小的事实。此外，由于检测置信度通常跨连续帧进行平均，且检测设备（如摄像头、激光雷达）常具有冗余性以提供容错能力，因此零星漏检并不重要。本文利用CBOD过程的这种内在容错性，通过汽车案例研究评估CBOD在多大程度能容忍来自低精度算术、近似算术单元甚至因低压运行等导致的随机故障等多源近似。我们证明，即使同时考虑三种近似领域，这些近似源对精度的影响仍保持在基线水平的1%以内，因此可借助多源近似来构建用于汽车CBOD的高效加速器。