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The combined use of multiple modalities enables accurate pedestrian detectionunder poor lighting conditions by using the high visibility areas from thesemodalities together. The vital assumption for the combination use is that thereis no or only a weak misalignment between the two modalities. In general,however, this assumption often breaks in actual situations. Due to thisassumption's breakdown, the position of the bounding boxes does not matchbetween the two modalities, resulting in a significant decrease in detectionaccuracy, especially in regions where the amount of misalignment is large. Inthis paper, we propose a multi-modal Faster-RCNN that is robust against largemisalignment. The keys are 1) modal-wise regression and 2) multi-modal IoU formini-batch sampling. To deal with large misalignment, we perform bounding boxregression for both the RPN and detection-head with both modalities. We alsopropose a new sampling strategy called "multi-modal mini-batch sampling" thatintegrates the IoU for both modalities. We demonstrate that the proposedmethod's performance is much better than that of the state-of-the-art methodsfor data with large misalignment through actual image experiments.

Multi-Modal Pedestrian Detection with Large Misalignment Based on Modal-Wise Regression and Multi-Modal IoU