GLARE: A Dataset for Traffic Sign Detection in Sun Glare
Real-time machine learning detection algorithms are often found within
autonomous vehicle technology and depend on quality datasets. It is essential
that these algorithms work correctly in everyday conditions as well as under
strong sun glare. Reports indicate glare is one of the two most prominent
environment-related reasons for crashes. However, existing datasets, such as
LISA and the German Traffic Sign Recognition Benchmark, do not reflect the
existence of sun glare at all. This paper presents the GLARE traffic sign
dataset: a collection of images with U.S based traffic signs under heavy visual
interference by sunlight. GLARE contains 2,157 images of traffic signs with sun
glare, pulled from 33 videos of dashcam footage of roads in the United States.
It provides an essential enrichment to the widely used LISA Traffic Sign
dataset. Our experimental study shows that although several state-of-the-art
baseline methods demonstrate superior performance when trained and tested
against traffic sign datasets without sun glare, they greatly suffer when
tested against GLARE (e.g., ranging from 9% to 21% mean mAP, which is
significantly lower than the performances on LISA dataset). We also notice that
current architectures have better detection accuracy (e.g., on average 42% mean
mAP gain for mainstream algorithms) when trained on images of traffic signs in
sun glare.