A Novel Detector for Wind Turbines in Wide-Ranging, Multiscene Remote Sensing Images

Abstract

Wind turbines are one of the important carriers of clean energy utilization. Accurately and rapidly detecting wind turbine objects in large-scale remote sensing images can effectively monitor the development activities and optimize energy utilization. Addressing the detection challenges posed by the complex distribution scenes and the slender, dispersed structural characteristics of wind turbines in remote sensing images, this article proposes a remote sensing image wind turbine detector, RSWDet, based on neural networks. RSWDet comprises two innovative key modules. The first is a dual-branch structured point set detection head, which, through training, adapts to the unique features of wind turbines, enabling accurate detection in large-scale complex backgrounds. The second is the Low-level Feature Enhancement module, which compensates for the loss of wind turbine feature information during sampling by leveraging rich low-level feature information. Experimental verification of RSWDet was conducted on datasets and real-world scenes. The results demonstrate that RSWDet exhibits significant advantages compared to other algorithms, achieving the highest average accuracy of 83.1%, Precision of 97.8%, and Recall of 99% on the validation set. In the actual multiscene GF2 remote sensing image test, with a threshold of 0.4, the Precision can reach 85.3%, and the Recall can reach 89.9%.

Keywords

• Deep neural network
• GF2
• multiple scenes
• remote sensing images
• wind turbines detection