Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesElectricity is indispensable to modern-day governments and citizenry’s day-to-day operations.
Fault identification is one of the most significant bottlenecks faced by Electricity transmission and
distribution utilities in developing countries to deliver credible services to customers and ensure
proper asset audit and management for network optimization and load forecasting. This is due to
data scarcity, asset inaccessibility and insecurity, ground-surveys complexity, untimeliness, and
general human cost. In this context, we exploit the use of oblique drone imagery with a high spatial
resolution to monitor four major Electric power transmission network (EPTN) components
condition through a fine-tuned deep learning approach, i.e., Convolutional Neural Networks
(CNNs). This study explored the capability of the Single Shot Multibox Detector (SSD), a onestage
object detection model on the electric transmission power line imagery to localize, classify
and inspect faults present. The components fault considered include the broken insulator plate,
missing insulator plate, missing knob, and rusty clamp. The adopted network used a CNN based
on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth
to localise and detect faults via a one-phase procedure. The SSD Rest50 architecture variation
performed the best with a mean Average Precision of 89.61%. All the developed SSD based
models achieve a high precision rate and low recall rate in detecting the faulty components, thus
achieving acceptable balance levels F1-score and representation. Finally, comparable to other
works of literature within this same domain, deep-learning will boost timeliness of EPTN inspection
and their component fault mapping in the long - run if these deep learning architectures are widely
understood, adequate training samples exist to represent multiple fault characteristics; and the
effects of augmenting available datasets, balancing intra-class heterogeneity, and small-scale
datasets are clearly understood
