Abstract: Arc additive manufacturing has attracted extensive attention due to its advantages of high deposition rate, short cycle time and low cost, but it is prone to defects such as cracks, porosity, and non-fusion in the manufacturing process. In this paper, we propose a fully automatic defect recognition and optimization compensation method that combines deep learning, visual sensing, image processing and six-axis industrial robots for real-time detection and repair of surface defects in the process of arc additive manufacturing. The surface of the workpiece is monitored by visual sensors, and the defects are identified by image processing algorithms, the start and end coordinates of the defects are accurately obtained, and the spatial distance is calculated, so as to quantify the size of the defects. In addition, the combination of deep learning models and six-axis industrial robots enables the robots to adjust the motion trajectory in real time based on visual feedback information to accurately compensate for defects of different types and sizes.