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Joint Image Enhancement and Localization Framework for Vehicle Model Recognition in the Presence of Non-Uniform Lighting Conditions

机译:在非均匀照明条件存在下车辆模型识别的联合图像增强与定位框架

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Recognizing the model of a vehicle in natural scene images is an important and challenging task for real-life applications. Current methods perform well under controlled conditions, such as frontal and horizontal view-angles or under optimal lighting conditions. Nevertheless, their performance decreases significantly in an unconstrained environment, that may include extreme darkness or over illuminated conditions. Other challenges to recognition systems include input images displaying very low visual quality or considerably low exposure levels. This paper strives to improve vehicle model recognition accuracy in dark scenes by using a deep neural network model. To boost the recognition performance of vehicle models, the approach performs joint enhancement and localization of vehicles for non-uniform-lighting conditions. Experimental results on several public datasets demonstrate the generality and robustness of our framework. It improves vehicle detection rate under poor lighting conditions, localizes objects of interest, and yields better vehicle model recognition accuracy on low-quality input image data.
机译:识别在自然场景图像中的车辆模型是真实应用的重要且具有挑战性的任务。电流方法在受控条件下执行良好,例如正面和水平视角或在最佳照明条件下。然而,它们的性能在不受约束的环境中显着降低,这可能包括极端黑暗或照明条件。识别系统的其他挑战包括输入图像,显示出非常低的视觉质量或相当低的曝光水平。本文通过使用深神经网络模型,努力提高暗场景中的车辆模型识别准确性。为了提高车辆模型的识别性能,该方法执行用于非均匀照明条件的车辆的联合增强和定位。几个公共数据集的实验结果展示了我们框架的一般性和鲁棒性。它在照明条件不佳下提高了车辆检测率,本地化感兴趣的对象,并在低质量输入图像数据上产生更好的车辆模型识别准确性。

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