Image-fusion-based object detection using a time-of-flight camera

Dongzhao Yang,Tianxu Xu,Yiwen Zhang,Dong An, Qang Wang, Zhongqi Pan,Guizhong Liu,Yang Yue

OPTICS EXPRESS(2023)

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摘要
In this work, we demonstrate an innovative object detection framework based on depth and active infrared intensity images fusion with a time-of-flight (ToF) camera. A slide window weight fusion (SWWF) method provides fuse image with two modalities to localize targets. Then, the depth and intensity information is extracted to construct a joint feature space. Next, we utilize four machine learning methods to achieve object recognition. To verify this method, experiments are performed on an in-house dataset containing 1066 images, which are categorized into six different surface materials. Consequently, the approach performs well on localization with a 0.778 intersection over union (IoU). The best classification results are obtained with K-Nearest Neighbor (KNN) with a 98.01% total accuracy. Furthermore, our demonstrated method is less affected by various illumination conditions.
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