SlantNet: A Lightweight Neural Network for Thermal Fault Classification in Solar PV Systems

The rapid growth of solar photovoltaic (PV) installations worldwide has increased the need for the effective monitoring and maintenance of these vital renewable energy assets. PV systems are crucial in reducing greenhouse gas emissions and diversifying electricity generation. However, they often experience faults and damage during manufacturing or operation, significantly impacting their performance, while thermal infrared imaging provides a promising non-invasive method for detecting common defects such as hotspots, cracks, and bypass diode failures, current deep learning approaches for fault classification generally rely on computationally intensive architectures or closed-source solutions, constraining their practical use in real-time situations involving low-resolution thermal data. To tackle these challenges, we introduce SlantNet, a lightweight neural network crafted to classify thermal PV defects efficiently and accurately. At its core, SlantNet incorporates an innovative Slant Convolution (SC) layer that utilizes slant transformation to enhance directional feature extraction and capture subtle thermal gradient variations essential for fault detection. We complement this architectural advancement with a thermal-specific image enhancement augmentation strategy that employs adaptive contrast adjustments to bolster model robustness under the noisy and class-imbalanced conditions typically encountered in field applications. Extensive experimental validation on a comprehensive solar panel defect detection benchmark dataset showcases SlantNet’s exceptional performance. Our method achieves a 95.1% classification accuracy while reducing computational overhead by approximately 60% compared to leading models.