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Pedestrian detection is an essential task in autonomous driving research. Inaddition to typical color images, thermal images benefit the detection in darkenvironments. Hence, it is worthwhile to explore an integrated approach to takeadvantage of both color and thermal images simultaneously. In this paper, wepropose a novel approach to fuse color and thermal sensors using deep neuralnetworks (DNN). Current state-of-the-art DNN object detectors vary fromtwo-stage to one-stage mechanisms. Two-stage detectors, like Faster-RCNN,achieve higher accuracy, while one-stage detectors such as Single Shot Detector(SSD) demonstrate faster performance. To balance the trade-off, especially inthe consideration of autonomous driving applications, we investigate a fusionstrategy to combine two SSDs on color and thermal inputs. Traditional fusionmethods stack selected features from each channel and adjust their weights. Inthis paper, we propose two variations of novel Gated Fusion Units (GFU), thatlearn the combination of feature maps generated by the two SSD middle layers.Leveraging GFUs for the entire feature pyramid structure, we propose severalmixed versions of both stack fusion and gated fusion. Experiments are conductedon the KAIST multispectral pedestrian detection dataset. Our Gated FusionDouble SSD (GFD-SSD) outperforms the stacked fusion and achieves the lowestmiss rate in the benchmark, at an inference speed that is two times faster thanFaster-RCNN based fusion networks.

GFD-SSD: Gated Fusion Double SSD for Multispectral Pedestrian Detection