  Object detection neural network models need to perform reliably in highly
dynamic and safety-critical environments like automated driving or robotics.
Therefore, it is paramount to verify the robustness of the detection under
unexpected hardware faults like soft errors that can impact a systems
perception module. Standard metrics based on average precision produce model
vulnerability estimates at the object level rather than at an image level. As
we show in this paper, this does not provide an intuitive or representative
indicator of the safety-related impact of silent data corruption caused by bit
flips in the underlying memory but can lead to an over- or underestimation of
typical fault-induced hazards. With an eye towards safety-related real-time
applications, we propose a new metric IVMOD (Image-wise Vulnerability Metric
for Object Detection) to quantify vulnerability based on an incorrect
image-wise object detection due to false positive (FPs) or false negative (FNs)
objects, combined with a severity analysis. The evaluation of several
representative object detection models shows that even a single bit flip can
lead to a severe silent data corruption event with potentially critical safety
implications, with e.g., up to (much greater than) 100 FPs generated, or up to
approx. 90% of true positives (TPs) are lost in an image. Furthermore, with a
single stuck-at-1 fault, an entire sequence of images can be affected, causing
temporally persistent ghost detections that can be mistaken for actual objects
(covering up to approx. 83% of the image). Furthermore, actual objects in the
scene are continuously missed (up to approx. 64% of TPs are lost). Our work
establishes a detailed understanding of the safety-related vulnerability of
such critical workloads against hardware faults.

Computer Vision and Pattern Recognition;Artificial Intelligence;Image and Video Processing