Detecting sun glint in UAV RGB images at different times using a deep learning algorithm

Unmanned aerial vehicle (UAV) remote sensing has played a crucial role in water quality monitoring. However, the presence of water sun glint, resulting from specular reflection on the water surface, poses an inevitable challenge in UAV-acquired images. These excessively bright pixels disrupt the original images' spectral and textural characteristics, significantly diminishing their usability. This disruption has repercussions on subsequent tasks, such as target object classification and water quality parameter inversion. Precise detection of sun glint is a prerequisite for removing them, but current methods suffer from missed and false detections. In this study, we collected images by UAV to construct a specialized dataset for water surface sun glint, namely water sun glint detection (WSGD) dataset, laying the groundwork for further research endeavors. We proposed the Res_AUNet network by enhancing the UNet convolutional neural network. The Convolutional Block Attention Module was integrated into the encoding-decoding skip connections of the network, we also refined the convolutional blocks to better capture the distinctive semantic features associated with water sun glint. To mitigate overfitting, the residual structures were incorporated and the number of convolutional kernels within each block was reduced. The Res_AUNet network was trained and evaluated using the WSGD dataset, achieving metrics with an Accuracy of 98.02%, an F1-score of 83.67%, and an IOU of 74.73%. These results underscore the precision of our proposed method for water sun glint detection in UAV water images, offering valuable insights for effectively eliminating water sun glint and determining the optimal timing for UAV water image acquisition.