  In the operation & maintenance (O&M) of photovoltaic (PV) plants, the early
identification of failures has become crucial to maintain productivity and
prolong components' life. Of all defects, cell-level anomalies can lead to
serious failures and may affect surrounding PV modules in the long run. These
fine defects are usually captured with high spatial resolution
electroluminescence (EL) imaging. The difficulty of acquiring such images has
limited the availability of data. For this work, multiple data resources and
augmentation techniques have been used to surpass this limitation. Current
state-of-the-art detection methods extract barely low-level information from
individual PV cell images, and their performance is conditioned by the
available training data. In this article, we propose an end-to-end deep
learning pipeline that detects, locates and segments cell-level anomalies from
entire photovoltaic modules via EL images. The proposed modular pipeline
combines three deep learning techniques: 1. object detection (modified
Faster-RNN), 2. image classification (EfficientNet) and 3. weakly supervised
segmentation (autoencoder). The modular nature of the pipeline allows to
upgrade the deep learning models to the further improvements in the
state-of-the-art and also extend the pipeline towards new functionalities.
