Automatic Assembly Inspection of Satellite Payload Module Based on Text Detection and Recognition

The payload module of a high-throughput satellite involves the complex assembly of various components, which plays a vital role in maintaining the satellite’s structural and functional integrity. To support this, inspections during the assembly process are essential for minimizing human error, reducing inspection time, and ensuring adherence to design specifications. However, the current inspection process is entirely manual. It requires substantial manpower and time and is prone to errors such as missed or false detections, which compromise the overall effectiveness of the inspection process. To enhance the inspection efficiency and accuracy of the payload module in high-throughput satellites, this paper proposes a framework for text detection and recognition targeting diamond labels, R-hole labels, and interface labels within payload module images. Detecting and recognizing text labels on products in the high-throughput satellite payload module provides a means to determine the individual products’ assembly states and the correctness of their connection relationships with the waveguides/cables. The framework consists of two key components: a copy-and-paste data augmentation method, which generates synthetic images by overlaying foreground images onto background images, together with a text detection and recognition model incorporating a dual decoder. The detection accuracy on the simulated payload module data reached 87.42%, while the operational efficiency improved significantly by reducing the inspection time from 5 days to just 1 day.