Assessment of the condition of underground
pipelines is crucial to avoid breakages. Autonomous in-line in-
spection tools provided with Non-destructive Technology (NDT)
sensors to assess large sections of the pipeline are commonly
used for these purposes. An example of such sensors based on
Eddy currents is the Remote Field Technology (RFT). A crucial
step during in-line inspections is the detection of construction
features, such as joints and elbows, to accurately locate and size
specific defects within pipe sections. This step is often performed
manually with the aid of visual data, which results in slow data
processing. In this paper, we propose a generic framework to
automate the detection and verification of these construction
features using both NDT sensor data and visual images. Firstly,
supervised learning is used to identify the construction features
in the NDT sensor signals. Then, image processing is employed
to verify the selection. Results are presented with data from a
RFT tool, for which a specialised descriptor has been designed
to characterise and classify its signal features. Furthermore,
the construction feature is displayed in the image, once it is
identified in the RFT data and detected in the visual data. A
visual odometry algorithm has been implemented to locate the
visual data with respect to the RFT data. About 800 meters of
these multi-modal data are evaluated to test the validity of the
proposed approach.