The lighting quality of the unmanned aerial vehicle (UAV) images is the key factor that affects the intelligent operation and maintenance efficiency in power system. When captured in low-light condition, the UAV images suffer from loss of details, leading to a decrease in object detection accuracy. However, the research is lacking with respect to low-light image enhancement for UAV-based overhead power transmission system images. In this letter, we propose a low-light image enhancement method based on Contrast Limited Adaptive Histogram Equalization (CLAHE) to robustify object detection against low-light condition. We first separate the luminance and chrominance channels of the UAV image via color space conversion so that CLAHE is used to enhance luminance contrast without introducing chrominance shift. Moreover, we optimize the contrast limit parameter in CLAHE to balance contrast enhancement and noise suppression, which further boosts object detection accuracy. Experimental results validate the effectiveness of the proposed scheme in detail enhancement without noise over-amplification, leading to a magnificent increase of insulator detection accuracy from 58.0% to 82.0%.

中文翻译：

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基于 CLAHE 的低光图像增强，用于架空输电系统中的鲁棒目标检测

【摘要】：无人机图像光照质量是影响电力系统智能运维效率的关键因素。在弱光条件下拍摄时，无人机图像会丢失细节，导致物体检测精度下降。然而，对于基于无人机的架空输电系统图像的弱光图像增强研究还很缺乏。在这封信中，我们提出了一种基于对比度受限自适应直方图均衡 (CLAHE) 的弱光图像增强方法，以加强针对弱光条件的物体检测。我们首先通过颜色空间转换分离无人机图像的亮度和色度通道，以便使用 CLAHE 来增强亮度对比度而不引入色度偏移。而且，我们优化了 CLAHE 中的对比度限制参数，以平衡对比度增强和噪声抑制，从而进一步提高目标检测精度。实验结果验证了所提出的方案在没有噪声过度放大的情况下细节增强的有效性，导致绝缘子检测精度从 58.0% 大幅提高到 82.0%。