The invention discloses a crawler crane anti-collision control system and method based on deep learning, and the system comprises a data collection module, a data processing module, a deep learning module, a control module and an execution module. The image acquisition module is used for acquiring image information of the crawler crane, converting the image information into digital signals and transmitting the digital signals to the data processing module, the data processing module is used for preprocessing the digital signals and performing feature extraction on the processed digital signals, and the deep learning module is used for classifying and identifying feature data of the surrounding environment of the crawler crane and transmitting an identification result to the control module; the control module is used for outputting a corresponding control instruction to the execution module according to the identification result, and the execution module is used for automatically adjusting the position and angle of the crawler crane according to the control instruction output by the control module. According to the invention, the crawler crane can automatically identify and avoid obstacles with different shapes and sizes, so that the safety performance and the working efficiency are improved.
本发明公开了一种基于深度学习的履带吊防碰撞控制系统及控制方法,包括数据采集模块,数据处理模块,深度学习模块,控制模块和执行模块,数据采集模块用于采集履带吊周围环境图像信息,并将图像信息转换为数字信号传输至数据处理模块,数据处理模块用于对数字信号进行预处理,以及对处理后的数字信号进行特征提取,深度学习模块用于对履带吊周围环境特征数据进行分类和识别,并将识别结果传输至控制模块,控制模块用于根据识别结果向执行模块输出相应的控制指令,执行模块用于根据控制模块输出的控制指令自动调整履带吊的位置和角度。本发明能实现了履带吊对不同形状、大小的障碍物的自动识别和避让,从而提高安全性能和工作效率。
Crawler crane anti-collision control system and control method based on deep learning
一种基于深度学习的履带吊防碰撞控制系统及控制方法
2023-09-08
Patent
Electronic Resource
Chinese
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