关键词: 可见光-热红外图像语义分割, 卷积神经网络, 图像先验信息, 光照感知算法, 特征增强和融合算法

Abstract:

In intelligent optoelectronic devices, the red greed blue-thermal (RGB-T) semantic segmentation tasks based on artificial intelligence can be widely applied in autonomous driving, drone aerial photography, video surveillance, and other fields. The image illumination prior information can be used to further improve the performance of semantic segmentation. An illumination perception and feature enhancement based RGB-T semantic segmentation model is presented. In the proposed model, those discriminative informations in input images can be highlighted, and those non-discriminative ones can be suppressed by employing the illumination prior information and attention mechanisms. The proposed model is compared with 12 state-of-the-art saliency models, including RGB, RGB-D and RGB-T semantic segmentation models on MFNet dataset. The quantitative evaluation metrics contain mean accuracy (mAcc) and mean intersection over union (mIoU). Compared with the second-best performing model, the proposed model achieves a 5.4% improvement in mAcc and a 1.0% improvement in mIoU. In addition, a series of ablation experiments in MFNet Dataset to are conducted to clearly show the effectiveness of different components in the proposed model. In this study, A new RGB-T semantic segmentation model, namely, illumination perception and feature enhancement network is proposed, which contains a illumination perception network, an attention interaction and feature enhancement module and a multi-scale feature interaction and fusion module. Experimental results on the public datasets demonstrate that the proposed model can achieve higher segmentation accuracy than some state-of-the-art models.

Key words: red greed blue-thermal image semantic segmentation, convolutional neural network, image prior information, illumination perception algorithm, feature enhancement and fusion algorithm