RL-DeepLabv3+: A lightweight rice lodging semantic segmentation model for unmanned rice harvester

Abstract

Lodging is a common natural hazard that occurs in rice during harvest. Currently, the unmanned rice harvester lacks a warning for lodging detection, which often results in harvesting omissions and machine blockages, degrading operational efficiency. Therefore, we proposed a lightweight semantic segmentation model RL-DeepLabv3 + for rice lodging detection of the unmanned rice harvester. We designed a backbone network Rice Lodging-Channel-wise Feature Pyramid (RL-CFP), replaced the Atrous Spatial Pyramid Pooling (ASPP) module with the Channel Attention-based Deep Separable Dilated Convolutional Pyramid (CD-ASP) module, and added the Channel Attention Module (CAM) to the decoding and encoding stages. We trained and evaluated the model using a homemade rice lodging dataset. The model size is 3.39 MB, and the number of parameters is 7.80 × 105. The mean intersection over union (mIoU) and mean pixel accuracy (mPA) of the model were 90.52 % and 94.73 %, which were higher than those of other comparable models. In application tests, when the input image resolutions were 640 × 480 and 1280 × 720, the model detection speeds were 50 frames/s and 21 frames/s in the vision system of the unmanned rice harvester. The proposed model has the advantages of high lodging detection accuracy, small model space occupation, and uncomplicated application deployment. It can provide a warning to the unmanned rice harvester for lodging detection and improve its intelligence level.