基于多模态RGB-T的显著性目标检测算法

Research on Multi-Modal RGB-T Based Saliency Target Detection Algorithm

针对RGB(Red Green Blue)模态与热度模态信息表征形式不一致,特征信息无法有效挖掘、融合问题,提出了一种新的联合注意力强化网络-FCNet(Feature Sharpening and Cross-modal Feature Fusion Net)。首先,通过双维度注意力机制提升图像特征映射能力;然后,利用跨模态特征融合机制捕获目标区域;最后,利用逐层解码结构消除背景干扰,优化检测目标。实验结果表明,该优化改进算法运算参数更少、运算时间更短,且模型整体检测性能均优于现有多模态检测模型性能。

To address the problem that RGB(Red Green Blue)modal and thermal modal information representations are inconsistent in form and feature information can not be effectively mined and fused,a new joint attention reinforcement network-FCNet(Feature Sharpening and Cross-modal Feature Fusion Net)is proposed.Firstly,the image feature mapping capability is enhanced by a two-dimensional attention mechanism.Then,a cross-modal feature fusion mechanism is used to capture the target region.Finally,a layer-by-layer decoding structure is used to eliminate background interference and optimize the detection target.The experimental results demonstrate that the improved algorithm has fewer parameters and shorter operation times,and the overall detection performance of the model is better than that of existing multimodal detection models.