基于深度学习的红外视频显著性目标检测

Deep Learning Based Salient Object Detection in Infrared Video

面对背景越来越复杂的海量红外视频图像,传统方法的显著性目标检测性能不断下降。为了提升红外图像的显著性目标检测性能,提出了一种基于深度学习的红外视频显著性目标检测模型。该模型主要由空间特征提取模块、时间特征提取模块、残差连接块以及像素级分类器4个模块组成。首先利用空间特征提取模块获得空间特征,然后利用时间特征提取模块获得时间特征并实现时空一致性,最后将时空特征信息和由残差连接块连接空间模块获得的空间低层特征信息一同送入像素级分类器,生成最终的显著性目标检测结果。训练网络时,使用BCEloss和DICEloss两个损失函数结合的方式,以提高模型训练的稳定性。在红外视频数据集OTCBVS以及背景复杂的红外视频序列上进行测试,结果表明所提模型都能够获得准确的显著性目标检测结果,并且具有鲁棒性及较好的泛化能力。

In the face of massive infrared video images with more and more complex background,the performance of the tradi-tional methods for salient object detection decreases significantly.In order to improve the performance of salient object detection in infrared images,this paper proposes a deep learning-based salient object detection model for infrared video,which mainly consists of a spatial feature extraction module,a temporal feature extraction module,a residual skip connection module and a pixel-wise classifier.First,the spatial feature extraction module is used to extract spatial saliency features from raw input video frames.Secondly,the temporal feature extraction module is used to obtain temporal saliency features and spatio-temporal coherence mo-deling.Finally,the spatial-temporal feature information and the spatial low-level feature information obtained by connecting the spatial module with the residual skip connection layer are sent into the pixel-wise classifier to generate the final salient object detection results.To improve the stability of the model,BCEloss and DICEloss are combined to train the network.The test is carried out on infrared video dataset OTCBVS and infrared video sequences with complex background.The proposed model can obtain accurate salient object detection results,and has robustness and good generalization ability.