A Low-Cost Defect Segmentation System Based on IoT for Large-Scale Photovoltaic Manufacturing

The photovoltaic industry is a strategic industry with international competitive advantages and is developing towards a larger scale, higher efficiency, and higher quality. However, current researchers have not built a pixel-level defect inspection system for large-scale photovoltaic production processes. This paper proposes an intelligent defect segmentation system combining the Internet of Things (IoT), artificial intelligence (AI), and edge computing for quality inspection of large-scale photovoltaic production lines. The intelligent factory based on this system is highly intelligent and deeply integrated, which can significantly reduce labor costs and improve factory productivity and product quality. The system's core uses edge computing to segment cells in real-time by a lightweight defect segmentation model. Specifically, this paper proposes a lightweight yet effective architecture named Low-cost Defect Segmentation Network (LDSN). An Efficient Split (ES) block is designed to support more channels and improve model accuracy without adding much computational complexity. Moreover, the ES block can express multiscale features in a finer granularity and enhance the information interaction between grouping features. In the decoding structure, a Dual Focus Attention (DFA) that efficiently captures long-range spatial and channel information is proposed. Comprehensive experiments have been performed on a low-end PC with an NVIDIA GeForce RTX3060 GPU and an Intel Core i5-10600KF. LDSN-T-Lite achieves 84FPS and the F-measure OIS of 0.827, which only has 166K parameters and 395.6M memory usage on our PSCDE1 dataset. A bigger version of LDSN-B achieves the F-measure OIS of 0.872, significantly outperforming current methods.