Abstract: In order to solve the problem of serious pollution caused by residual plastic film during cotton planting， a fast recognition and segmentation method based on improved SegFormer model is proposed. Taking the collected surface residual film of cotton field in Changji City， Xinjiang Uygur Autonomous Region （coordinates 44 ° 23 ′ 1 ″ N， 87 ° 30 ′ 23 ″ E） as the research object， 1047 images are collected at noon on a sunny day after snow and made into a data set. Based on the SegFormer model， a deeper feature layer level is added to obtain more subtle features to solve the problem of the morphologic variation of the residual film and the smaller target. The average crossing and merging ratio of the original SegFormer model has reached 83.00%， the average crossing and merging ratio of the improved SegFormer model has increased by 0.42 percentage points compared with the original model， the die coefficient has increased by 0.3 percentage points and the single detection time is 51.13 ms. The experimental results show that the improved SegFormer model can basically meet the requirements of fast segmentation tasks， and provide a theoretical basis for rapid assessment of residual film pollution in cotton fields.

Key words: residual film in cotton fields, UAV, neural networks, semantic segmentation, SegFormer, feature map level