The application of real-time monitoring has been widely used to detect the safety and stability of the electric power system. Traditional monitoring relies heavily on human judgment and is impossible to detect status in real-time. Recently, with the development of deep learning, the object detection algorithm based on the deep convolutional neural network becomes a great option for realizing real-time monitoring applications of the power system. However, in power system scenarios, failed or unreal-time detection of abnormal conditions may cause a hazardous accident. To apply and optimize the object detection algorithm, issues such as multi-scale objects, class imbalance, and difficulty in balance speed and accuracy need to be addressed to improve the detection performance. Thus, we present a cascaded feature enhancement network model that combining attention mechanism, feature fusion scheme, and Cascaded Refinement Scheme. Attention mechanism and feature fusion scheme can help extract more effective feature information of multi-scale objects. Cascaded Refinement Scheme can effectively solve the problem of class imbalance. The whole model can well balanced in detect speed and accuracy. Experiments are performed on two benchmarks: PSA_Datasets and PASCAL VOC. Our method gets an absolute gain of 1.6% (300300 input), 2.6% (512512 input) in terms of mAP result of PSA_Datasets and 1% (300300 input), 1.6% (512512 input) in PASCAL VOC Dataset, compared to the best results of other SOTA detectors.

中文翻译：

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电力系统实时视频监控的级联特征增强网络模型

实时监测的应用已广泛用于检测电力系统的安全性和稳定性。传统监控严重依赖人的判断，无法实时检测状态。近年来，随着深度学习的发展，基于深度卷积神经网络的目标检测算法成为实现电力系统实时监控应用的一个很好的选择。然而，在电力系统场景中，异常情况的失败或不实时检测可能会导致危险事故。为了应用和优化目标检测算法，需要解决多尺度目标、类别不平衡以及难以平衡速度和准确性等问题，以提高检测性能。因此，我们提出了一种结合注意力机制、特征融合方案和级联细化方案的级联特征增强网络模型。注意力机制和特征融合方案可以帮助提取多尺度物体更有效的特征信息。级联细化方案可以有效解决类别不平衡的问题。整个模型在检测速度和精度上达到了很好的平衡。实验在两个基准上进行：PSA_Datasets 和 PASCAL VOC。与最佳方法相比，我们的方法在 PSA_Datasets 的 mAP 结果方面获得了 1.6%（300300 个输入）、2.6%（512512 个输入）的绝对增益，在 PASCAL VOC 数据集中获得了 1%（300300 个输入）、1.6%（512512 个输入）的绝对增益其他 SOTA 检测器的结果。