  Electrical distribution poles are important assets in electricity supply.
These poles need to be maintained in good condition to ensure they protect
community safety, maintain reliability of supply, and meet legislative
obligations. However, maintaining such a large volumes of assets is an
expensive and challenging task. To address this, recent approaches utilise
imagery data captured from helicopter and/or drone inspections. Whilst reducing
the cost for manual inspection, manual analysis on each image is still
required. As such, several image-based automated inspection systems have been
proposed. In this paper, we target two major challenges: tiny object detection
and extremely imbalanced datasets, which currently hinder the wide deployment
of the automatic inspection. We propose a novel two-stage zoom-in detection
method to gradually focus on the object of interest. To address the imbalanced
dataset problem, we propose the resampling as well as reweighting schemes to
iteratively adapt the model to the large intra-class variation of major class
and balance the contributions to the loss from each class. Finally, we
integrate these components together and devise a novel automatic inspection
framework. Extensive experiments demonstrate that our proposed approaches are
effective and can boost the performance compared to the baseline methods.
