As the need of power supply is tremendously increasing in modern society, the stableness and reliability of the power delivery system are the two essential factors that ensure the power supply safety. With the quick expansion of electricity infrastructures, the failures of power transmission system are becoming more frequent, leading to economic loss and high risk of maintenance work under hazardous conditions. The existing automatic power line inspection utilizes advanced convolutional neural network (CNN) to improve the inspection efficiency, emerging as one promising solution. But the needed computational complexity is high since CNN inference demands large amount of multiplication-and-accumulation operations. In this paper, we alleviate this problem by utilizing the heterogeneous computing techniques to design a real-time on-site inspection system. Firstly, the required computational complexity of CNN inference is reduced using FFT-based convolution algorithms, speeding up the inference. Then we utilize the region of interest (ROI) extrapolation to predict the object detection bounding boxes without CNN inference, thus saving computing power. Finally, a heterogeneous computing architecture is presented to accommodate the requirements of proposed algorithms. According to the experiment results, the proposed design significantly improves the frame rate of CNN-based inspection visual system applied to power line inspection. The processing frame rate is also drastically improved. Moreover, the precision loss is negligible which means our proposed schemes are applicable for real application scenarios.
TOWARDS ROBUST HUMAN MILLIMETER WAVE IMAGING INSPECTION SYSTEM IN REAL TIME WITH DEEP LEARNING
