Solar Panels Detection of High-Resolution Aerial Images Based on Improved Faster-RCNN

Detecting and counting solar panels from high-resolution aerial images timely and accurately is essential for monitoring and management of industrial solar photovoltaic (PV) systems. Due to the influence of weather and light, the detection results of traditional methods are usually unsatisfactory. For the purpose of improving detection accuracy, we propose a method that combined residual network and channel attention module to improve the Faster RCNN framework. First, the balance between the training data size and model complexity is investigated, and the residual network is utilized to deepen the feature extractor within the effective range. Then, the channel attention modules are introduced into the network to further enhance the feature representation. Experimental results, conducted on high-resolution aerial image over Guilin, China, prove that the proposed method can detect solar panels with better accuracy than other related methods.