Improving power transmission tower state recognition in remote sensing images using cooperative Adaboost-MobileNet

Abstract

ABSTRACT Tower state recognition is of great importance in smart electric power management. Most previous efforts were devoted on the guyed towers and tower structures or materials to monitor the structural stability of towers through sensors fixed on them. Actually, identifying their states as a whole is also needed for remote management. This study addresses the task of tower state recognition in high-resolution remote-sensing images under the framework of deep learning. To achieve this goal, a new remote-sensing image dataset is constructed. Technically then, an ensemble learning strategy is developed to classify the states of the transmission towers. Specifically, MobileNetV3 is taken as the weak classifier as it is simple yet parameter-light in the family of convolutional networks. Along the line of ensemble learning with the principal of Adaboost in machine learning, an Adaboost-style network paradigm, named as Adaboost-MobileNetV3 in this paper, is finally proposed to recognize the states of towers. To evaluate its effectiveness, a series of deep convolutional networks are compared with the proposed network. The extensive comparative experiments indicate that the proposed Adaboost-MobileNetV3 network is suitable for practical tower state recognition, which achieves about 90% accuracy with at least about 4% higher than these compared models.