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Photovoltaic cells are electronic devices that convert light energy toelectricity, forming the backbone of solar energy harvesting systems. Anessential step in the manufacturing process for photovoltaic cells is visualquality inspection using electroluminescence imaging to identify defects suchas cracks, finger interruptions, and broken cells. A big challenge faced byindustry in photovoltaic cell visual inspection is the fact that it iscurrently done manually by human inspectors, which is extremely time consuming,laborious, and prone to human error. While deep learning approaches holds greatpotential to automating this inspection, the hardware resource-constrainedmanufacturing scenario makes it challenging for deploying complex deep neuralnetwork architectures. In this work, we introduce CellDefectNet, a highlyefficient attention condenser network designed via machine-driven designexploration specifically for electroluminesence-based photovoltaic cell defectdetection on the edge. We demonstrate the efficacy of CellDefectNet on abenchmark dataset comprising of a diversity of photovoltaic cells capturedusing electroluminescence imagery, achieving an accuracy of ~86.3% whilepossessing just 410K parameters (~13$\times$ lower than EfficientNet-B0,respectively) and ~115M FLOPs (~12$\times$ lower than EfficientNet-B0) and~13$\times$ faster on an ARM Cortex A-72 embedded processor when compared toEfficientNet-B0.

CellDefectNet: A Machine-designed Attention Condenser Network for Electroluminescence-based Photovoltaic Cell Defect Inspection