Significant progress has been made in the field of cotton seedling and weed detection using deep learning-based ob-ject detection techniques.This article proposes an improved Detection Transformer-based model for cotton seedling and weed detec-tion to improve the accuracy and efficiency of weed target detection.Firstly,a deformable attention module is introduced to replace the Transformer attention module in the original model,improving the model's ability to handle feature map object deformation.A new denoising training mechanism is proposed to address the unstable bipartite graph matching problem.A hybrid query selection strategy is proposed to improve the decoder's utilization efficiency of target category and position information.The Swin Transformer is used as the network backbone to enhance the model's feature extraction ability.By comparing with the original network,the pro-posed model demonstrates a faster convergence speed during training and improves accuracy by 6.7%.
维普
万方数据