Recently, significant breakthroughs have been achieved in the field of object detection. However, existing methods mostly focus on the generic object detection task. Performance degradation can be unavoidable when applying the existing methods to some specific situations directly, e.g. a low-light environment. To address this issue, we propose a single-shot real-time object Detector based on Low-light image Enhancement, namely LEDet. LEDet adapts itself to the low-light detection task in three aspects. First, a low-light enhancement module is introduced as the image preprocessor, producing the augmented inputs from the low-light images. Second, two modules, i.e. low-light and enhanced features fusion module and the scale-aware channel attention dilated convolution module are designed. These two modules aim at learning robust and discriminative features from objects of various sizes hidden in the darkness. In experiments, we validate the effectiveness of each part of our LEDet model via several ablation studies. We also compare LEDet with various methods on the Exclusively Dark dataset, showing that our model achieves the state-of-the-art performance on the balance between speed and accuracy.

中文翻译：

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LEDet：基于微光图像增强的单次实时目标检测器

最近，目标检测领域取得了重大突破。然而，现有方法主要集中在通用对象检测任务上。将现有方法直接应用于某些特定情况（例如弱光环境）时，性能下降是不可避免的。为了解决这个问题，我们提出了一种基于低光图像增强的单次实时目标检测器，即 LEDet。LEDet 在三个方面适应弱光检测任务。首先，引入了低光增强模块作为图像预处理器，从低光图像中产生增强的输入。其次，设计了两个模块，即低光和增强特征融合模块和尺度感知通道注意力扩张卷积模块。这两个模块旨在从隐藏在黑暗中的各种大小的物体中学习鲁棒和有区别的特征。在实验中，我们通过多项消融研究验证了 LEDet 模型每个部分的有效性。我们还将 LEDet 与 Exclusively Dark 数据集上的各种方法进行了比较，表明我们的模型在速度和准确性之间取得了最先进的性能。