A Lightweight Transformer Model for Defect Detection in Electroluminescence Images of Photovoltaic Cells
Yang Yang, Jing Zhang, Xin Shu, Lei Pan, Ming Zhang
Abstract
Solar panels play a crucial role in converting solar energy into electricity, with PhotoVoltaic (PV) modules being their core components. To ensure solar panels function well, efficient and accurate defect detection of PV modules is essential. Visual-based deep learning detection methods, such as Transformer and Convolutional Neural Network (CNN) models, provide a cost-effective and adaptable solution. Although the Transformer-based detectors outperform CNN-based ones in detection tasks, their complicated model structures also lead to great computational cost and latency, limiting their application in resource-constrained environments or time-sensitive tasks. To improve the real-time performance of the DEtection TRansformer (DETR) model on these edge devices, a novel Lightweight Decoder-only DETR (LD-DETR) is proposed for defect detection of PV modules, which achieves high performance and low computational cost with an easy architecture. We remove the resource-intensive Transformer Encoder structure from the DETR baseline so that the Decoder can directly utilize the finely fused features produced by a specialized lightweight convolution module that includes an upsample operation to preprocess the features. Experiments demonstrate that the proposed LD-DETR achieves competitive performance on the PV cell dataset of ElectroLuminescence (EL) images with an accuracy of 87.4% and an inference time of 6.1ms on GPU as well as 1.1s on embedded CPU. Our source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SeveralYang/LD-DETR</uri>.