Cascade CenterNet: Robust Object Detection for Power Line Surveillance

Abstract

The use of object detection methods is becoming increasingly important in the internet of smart energy. Accurate detection of power line region and suspicious objects can reduce the probability of accidents and save manpower. However, the noisy outdoor background and time-varying lighting conditions bring difficulties to those tasks. To address those challenges, this paper investigates mainstream methods of power line region surveillance and proposes a new method for improving the accuracy of power line region and risk object detection by modeling the heat map branch of CenterNet, which is the most representative of anchor-free detectors, with a cascade guiding structure and an improved loss function. Finally, comprehensive experiments are conducted to compare some state-of-the-art detectors and the proposed model on power line surveillance tasks. The test results demonstrate both the effectiveness and superiority of the proposed method.

Keywords

• Convolutional neural networks
• deep learning
• object detection
• electric transmission line