WLUSNet: A lightweight wheat lodging segmentation network based on UAV image

Abstract

Wheat lodging is a usual agricultural disaster in wheat growth. It reduces the grain yield and harvesting efficiency. Existing segmentation methods cannot achieve satisfactory performance and trade-offs between accuracy, inference time, and lightweight when facing the challenge of multiple lodging scenes. Therefore, developing an innovative segmentation algorithm that is real-time and low-complexity to identify lodging situations is of great value for improving agricultural production. To achieve these goals, we propose a lightweight and efficient lodging semantic segmentation model, WLUSNet, to separate the lodging area of unmanned aerial vehicle (UAV) images. Inspired by the mixed depth-wise grouping convolution (MC) and the channel feature pyramid (CFP) modules, a multiscale backbone (MC-CFP) is designed to reduce information loss in feature extraction. Then, drawing on the characteristics of MC and the channel attention (CA) mechanism, a space pyramid module (MC-SP) is designed to enhance feature representation by obtaining the global information on the channel feature and the local information on the space feature. To reconstruct a high-resolution feature map, a feature fusion module (EDFF) between the shallow and deep features is introduced to improve segmentation accuracy. The comprehensive experimental results demonstrate that WLUSNet performs excellently well compared with 11 other state-of-the-art (SOTA) segmentation algorithms. WLUSNet achieves a mean intersection over union (mIoU) of 86.9, a mean pixel accuracy (mPA) of 93.26, a model size of 4.1 M, and an inference speed of 26.94 FPS on the self-built UAV remote sensing dataset in this paper. The generation experiment indicates that WLUSNet has the potential to segment other lodging crops, and can provide technical support for segmentation tasks in crop lodging.