  Object detection with multimodal inputs can improve many safety-critical
perception systems such as autonomous vehicles (AVs). Motivated by AVs that
operate in both day and night, we study multimodal object detection with RGB
and thermal cameras, since the latter can provide much stronger object
signatures under poor illumination. We explore strategies for fusing
information from different modalities. Our key contribution is a non-learned
late-fusion method that fuses together bounding box detections from different
modalities via a simple probabilistic model derived from first principles. Our
simple approach, which we call Bayesian Fusion, is readily derived from
conditional independence assumptions across different modalities. We apply our
approach to benchmarks containing both aligned (KAIST) and unaligned (FLIR)
multimodal sensor data. Our Bayesian Fusion outperforms prior work by more than
13% in relative performance.
