针对变截面T梁模板打磨工作难度大，费时长等问题，设计一种基于深度学习的T梁模板打磨机器人。首先提出一种自适应打磨结构，解决了现有打磨装置无法贴合T梁模板内侧变截面，打磨辊容易卡在T梁模板隔断内的技术问题；其次为实现打磨质量的量化监测，提出一种YOLOv8n-DSE算法，对模板上的混凝土痂块及污渍进行识别，引入DySample动态上采样模块，增强模型的抗干扰能力并加快计算速度；为提高小目标检测的精度，设计SOEP（small object enhance pyramid）模块，通过SPDConv（space to depth convolution）获得小目标信息特征给到CSP（cross stage partial）-Omni-Kernel进行特征整合提高小目标的检测检测性能；最后替换EMA（exponential moving average）-Slide Loss让模型更加关注困难目标，可以改善在难例检测上的效果。准确率、召回率和均值平均精度（mean average precision, mAP）值相较改进前分别提升了3.1%，9.7%和3.2%，将改进的模型部署至机器人并进行现场测试。结果表明：设备满足工厂对变截面T梁模板的打磨需求。

A deep learning based T-beam formwork polishing robot was designed for the problems of difficult and time consuming polishing of T-beam formwork for variable cross-section. Firstly, an adaptive polishing structure was proposed to solve the technical problem that the existing polishing device cannot fit the inner variable cross-section of the T-beam formwork, and the polishing roller was easy to get stuck in the T-beam formwork partition. Secondly, in order to realize the quantitative monitoring of the polishing quality, a YOLOv8n-DSE algorithm was proposed to identify concrete dirt and stains on the formwork, the DySample dynamic up-sampling module was introduced to enhance the anti-interference ability of the model and accelerate the calculation speed, to improve the accuracy of small target detection, the SOEP (small object enhance pyramid) module was designed to improve the detection performance of small target detection through the SPDConv(space to depth convolution) to obtain the information features of the small target and give them to the CSP(cross stage partial)-Omni-Kernel for the integration of the features. Finally, the EMA(exponential moving average)-SlideLoss was replaced to make the model more concerned with the quantitative monitoring of the concrete, allowed the model to focus more on difficult targets, which can improve the effect on difficult case detection. The accuracy, recall, and mAP(mean average precision) values are improved by 3.1%, 9.7%, and 3.2%, respectively, compared with those before the improvement. The improved model was deployed to the robot and tested in the field. The results show that the equipment meets the plant's needs for polishing variable-section T-beam formwork.