起重运输机械Issue(6)：34-40,7.
基于深度学习的智能叉车障碍物识别与定位算法
摘要
Abstract
Accurate identification and location of obstacles by forklifts are crucial for enhancing the automation and intelligence of the logistics and warehousing industry.However,due to the complex scenarios encountered in production,most existing technologies fail to meet the stringent requirements for high-precision,real-time obstacle detection and localization.To address these challenges,an intelligent forklift obstacle identification and location method based on deep learning is proposed.This method aims to achieve real-time,high-precision obstacle identification and localization within the warehouse yard environment.Initially,an enhanced YOLOX-nano network was designed to identify and extract obstacles by obtaining their bounding rectangles.Subsequently,an improved DeepLabV3+network was employed to segment the detected obstacles.Finally,a threshold segmentation method was adopted to extract the contours of the segmented obstacle areas,and the pixel center of each obstacle was calculated.Experiments conducted using an obstacle dataset collected from the actual working environment of forklifts demonstrate that this method can swiftly and accurately locate the centers of obstacles.In particular,the first stage of obstacle detection achieved an average accuracy of 99.90%,with an average detection time of 25.62 ms.In the second stage,the average pixel accuracy of obstacle segmentation reached 95.93%,and the average segmentation time was 24.69 ms.These results demonstrate that this method can ensure real-time obstacle identification with high accuracy,thereby significantly enhancing the safety and efficiency of forklift operations.关键词
智能叉车/目标检测/目标分割/障碍物识别/目标定位Key words
intelligent forklift/target detection/target segmentation/obstacle identification/target location分类
机械制造引用本文复制引用
赵钢,曹男,曹轶伦..基于深度学习的智能叉车障碍物识别与定位算法[J].起重运输机械,2025,(6):34-40,7.基金项目
国家自然科学基金(51675450) （51675450）
