The classification of visible issues on Over-Head Line [OHL] inspection for preventive maintenance is a valuable and time consuming task. Traditional image processing algorithms have been used for the automatic detection, either assisted or complemented by human experts for severity classification. In recent years, machine learning has been proposed as a fast-track alternative to automate this task at the expense of huge data volumes (more than 1GB per km of OHL track). The proposed contribution compares the results of both kinds of algorithms for detection and classification of four visible Points of Interest [PoI] of different natures: stork nests on towers, aerial warning spheres, number plates and worn fittings on suspension towers (suspension ball eyes and shackles).The author works on the development of technology solutions across all the inspection process, from the data collection stage to data analytics and asset management information systems. It reviews his team results on the particular issues of image based inspection, considering success rates for both computer vision and machine learning solutions.The comprehensive data to information chain is discussed from data collection, analytics, information management and later, maintenance actions, auditing and long term maintenance planning. In addition to the challenges of data management in such volumes and improving the efficiency of inspection, it is essential to measure the actual benefits utilities across the asset maintenance cycle. A discussion on the multi-variable risk-index associated with severity and exposure, the combination of issues and overall condition assessment is introduced.