农业机械学报2025，Vol.56Issue(5)：111-120,10.DOI:10.6041/j.issn.1000-1298.2025.05.011
基于改进YOLO 11n模型的棉花田间复杂环境障碍物检测方法
Obstacle Detection Method for Complex Cotton Field Environments Based on Improved YOLO 11n Model
摘要
Abstract
Aiming to address the challenges of accurate obstacle detection in complex cotton field environments due to occlusions and the computational limitations of edge devices,a field obstacle detection method based on improved YOLO 11n model was proposed.Firstly,the lightweight StarNet network was adopted as the primary feature extraction network,and the dynamic position bias attention block module(DBA)was introduced to reconstruct convolutional block with parallel spatial attention(C2PSA)to enhance multi-scale feature interaction.Secondly,Kolmogorov-Arnold generalized network convolution(KAGNConv)was used to replace the bottleneck structure in the cross stage partial with kernel size 2 module(C3k2)of the baseline model,enabling fine-grained feature extraction while improving model flexibility and interpretability.Finally,the separated and enhancement attention module(SEAM)was integrated into the detection head to enhance the model's detection capability in occlusion scenarios.The experimental results showed that,compared with the baseline model,the improved YOLO 11n-SKS achieved increases of 2.3,2.1,1.3,and 1.4 percentage points in precision,recall,mAP50,and mAP50_95,reaching 91.7％,88.3％,91.9％,and 62.3％,respectively.The model's floating-point operations were reduced to only 4.4 × 109 FLOPs,and the number of model parameters was decreased by 17.1％.This study achieved a favorable balance between performance and computational complexity,meeting the real-time detection requirements of cotton harvesting operations while lowering the computational demands for deployment on edge devices,thereby providing technical support for the autonomous and safe operation of cotton pickers.关键词
采棉机/障碍物检测/深度相机/YOLO 11n模型/目标识别Key words
cotton picker/obstacle detection/depth camera/YOLO 11n model/object recognition分类
信息技术与安全科学引用本文复制引用
韩科立,王振坤,余永峰,刘淑平,韩树杰,郝付平..基于改进YOLO 11n模型的棉花田间复杂环境障碍物检测方法[J].农业机械学报,2025,56(5):111-120,10.基金项目
国家重点研发计划项目(2022YFD2002402)和中国机械工业集团有限公司重大科技专项(ZDZX2022-1) （2022YFD2002402）
