College of Computer and Information, Hohai University, Nanjing 210098, China.
Jiangsu Collaborative Innovation Center for Cultural Creativity, Changzhou 213000, China.
Sensors (Basel). 2018 Sep 30;18(10):3286. doi: 10.3390/s18103286.
Foggy days pose many difficulties for outdoor camera surveillance systems. On foggy days, the optical attenuation and scattering effects of the medium significantly distort and degenerate the scene radiation, making it noisy and indistinguishable. Aiming to solve this problem, in this paper we propose a novel object detection method that has the ability to exploit the information in the color and depth domains. To prevent the error propagation problem, we clean the depth information before the training process and remove false samples from the database. A domain adaptation strategy is employed to adaptively fuse the decisions obtained in the color and depth domains. In the experiments, we evaluate the contribution of the depth information for object detection on foggy days. Moreover, the advantages of the multiple-domain adaptation strategy are experimentally demonstrated via comparison with other methods.
雾天给户外摄像机监控系统带来了许多困难。在雾天，介质的光学衰减和散射效应会显著扭曲和退化场景辐射，导致图像变得嘈杂和难以辨认。针对这个问题，本文提出了一种新的物体检测方法，该方法能够利用颜色和深度域中的信息。为了防止误差传播问题，我们在训练过程之前对深度信息进行清理，并从数据库中删除错误样本。采用域自适应策略自适应融合颜色和深度域中的决策。在实验中，我们评估了深度信息对雾天物体检测的贡献。此外，通过与其他方法的比较，实验证明了多域自适应策略的优势。
