论文标题
大气湍流场景中的物体识别
Object recognition in atmospheric turbulence scenes
论文作者
论文摘要
大气湍流对获得的监视图像的影响在图像解释和场景分析中构成了重大挑战。在这种情况下，用于目标分类和跟踪的常规方法效率较低。虽然基于深度学习的对象检测方法在正常条件下表现出巨大的成功，但它们不能直接应用于大气湍流序列。在本文中，我们提出了一个新颖的框架，该框架学习了扭曲的特征，以检测和对湍流环境中的对象类型进行分类。具体而言，我们利用可变形的卷积来处理空间湍流位移。使用特征金字塔网络提取功能，并将更快的R-CNN用作对象检测器。合成VOC数据集的实验结果表明，所提出的框架以平均平均精度（MAP）得分超过30％的基准优于基准。此外，实际数据的主观结果显示出绩效的显着改善。
The influence of atmospheric turbulence on acquired surveillance imagery poses significant challenges in image interpretation and scene analysis. Conventional approaches for target classification and tracking are less effective under such conditions. While deep-learning-based object detection methods have shown great success in normal conditions, they cannot be directly applied to atmospheric turbulence sequences. In this paper, we propose a novel framework that learns distorted features to detect and classify object types in turbulent environments. Specifically, we utilise deformable convolutions to handle spatial turbulent displacement. Features are extracted using a feature pyramid network, and Faster R-CNN is employed as the object detector. Experimental results on a synthetic VOC dataset demonstrate that the proposed framework outperforms the benchmark with a mean Average Precision (mAP) score exceeding 30%. Additionally, subjective results on real data show significant improvement in performance.
