Defect Detection of Photovoltaic Panels Based on Deep Learning

The article proposes a high-precision algorithm for detecting defects in photovoltaic panels, which can detect and classify damaged areas in the images. The algorithm uses a parallel cross-encoding and decoding detection network, combining Transformer and convolutional models to extract and interactively transmit features, optimizing the calculation process and enhancing the network's perception of global and local features. Experimental tests show that the detection accuracy reaches 92.0%, which is far superior to similar detection networks.