To fully utilize the abundance of annotated RGB image data and improve the performance of infrared object detection,an unsupervised domain adaptation(UDA)method based on deep learning model was proposed.The Faster R-CNN backbone network was modified to improve the receptive field representation capability,the positive and negative sample imbalance problem of bounding boxes was alleviated,and the regression mechanism was optimized.To address the domain shift problem at different levels during the transfer from the RGB domain to the infrared domain,both image-level and instance-level feature distribution alignments at different network layers and stages of the enhanced Faster R-CNN architecture were proposed.Experimental results demonstrate that using the proposed UDA method significantly improves object detection performance,achieving 73.35％and 77.66％mean average precision(mAP)on the KAIST and FLIR-ADAS multi-spectral public datasets,respectively,without the need for infrared image annotations.
维普
万方数据