Petards Rail, Network Rail, Greater Anglia, and CrossTech’s innovative partnership addresses industry-wide vegetation management issues through the application of next generation technology… and has resulted in several rail industry firsts.
We are proud to be collaborating with Network Rail, Greater Anglia and CrossTech to proactively address an industry-wide issue, in a partnership which has resulted in many ‘firsts’ for the global rail industry.
The collaborative approach harnesses the power of data, innovative technology, computer vision AI and cross-agency communication to identify and address vegetation growth and contamination on rail lines. This enhances operating efficiency for passengers and freight users and increases safety for track workers.
The Issue
Vegetation growth and contamination poses a risk to the safe and seamless operation of a railway. For train operators, such as Greater Anglia, vegetation growth and contamination can lead to delays or train faults/damage, which in turn can impact passenger satisfaction and the wider economy.
Vegetation and lineside management can be a complex issue for Network Rail, which undertakes the interventions. The organisation must not only deliver a quick resolve to the issue, to minimise the risk of asset and line damage, but also consider the impact on the wider environmental aspects – all the while prioritising worker safety in what can be a hazardous environment. What’s more, issues caused by vegetation growth can include impeded sight lines and signals, and damaged infrastructure, which impacts on services and reliability.
The Solution
The collaborative approach harnesses the power of data, innovative technology, computer vision AI and cross-agency communication to identify and address vegetation growth and contamination on rail lines. This enhances operating efficiency Network Rail and Greater Anglia are tackling this problem by using video recorded on the Greater Anglia trains, using Petards Rail’s eyeTrain technology, to identify which areas are becoming overgrown. The high-definition and robust forward-facing cameras fitted to Greater Anglia’s new trains (both 745s and 720s) record data on the Great Eastern main line between London and Norwich and the West Anglia main line between London and Cambridge.
Using our eyeTrain Connect analytics and data transfer system; the train, route, location, and video footage are automatically identified, and the necessary data transferred to Hubble, CrossTech’s Hubble AI computer vision technology, for processing. This removes the need for manual processing of data off the train.
Hubble – which was developed in close conjunction with Network Rail end users – then undertakes an automated inspection covering a raft of inspection categories including, but not limited to, vegetation encroachment, signal and level crossing sighting, safe access pathways, and optimal height of overhead line counterbalance weights. Using custom algorithms, the technology analyses and inspects the camera video feeds and recordings to detect problem areas on the rail infrastructure. Maps are then generated showing where issues have been found and alerts are delivered to Network Rail front line delivery units, based on Network Rail’s standards and requirements. This allows Network Rail engineers to pre-emptively address the areas and prioritise issues depending on their severity.
Through the joint solution, Network Rail can identify the safest, most efficient intervention method and clearance approach remotely – as well as the equipment and manpower required – further increasing safety for track workers and ensuring an efficient response.
For Greater Anglia operations and assets are then not disrupted by vegetation growth and contamination. This – alongside the company’s wider efforts – means fewer instances of service disruption and train faults, helping to improve service reliability, thereby ultimately improving customer satisfaction.
The solution, which utilises the expertise of each of the organisations and is reliant on a combined effort, not only uses advanced and innovative technology to proactively remedy an industry-wide issue, but it is delivered in line with industry best practice. For example, the Petards Rail cameras which are utilised in this project comply with GM/RT2606, EN 50155, EN 50121, The British Transport Police guidelines, EN 50132-7, HOSBD, BS EN ISO 14001:2004 (EMS 534323), and PAS 99:2012 (IMR 534323).
A practical approach to a prevalent issue, the end-to-end solution utilises technology and data which is already in place to improve resilience on rail lines.
The Outcome
The cross-industry collaboration, which initially commenced in autumn of 2022, has to date seen numerous vegetation issues or potential issues identified and addressed, helping to underpin Greater Anglia’s excellent annual punctuality which is currently at 93.5% PPM and 92.5% T-3.
The successful collaboration has led to initial steps towards the extension of the same approach across Network Rail’s Eastern Region and is a gold standard example of what local cross-industry partnerships can achieve.
It has resulted in several world firsts for the railway including:
- This is the first time that multiple parties from the rail industry, which includes technology SMEs, have come together to achieve a continuous remote data feed for this type of information and then analyse this data using a next generation AI computer vision asset inspection system on a repetitive and standardised basis, to enable better lineside management.
- The first full fleet deployment with remote integration between camera systems and the Hubble AI computer vision system.
- The first use of an automated invasive species detection and signal sighting early warning system and the first operational deployment of an AI computer vision inspection and fault detection system as business as usual.
Martin Beable, Engineering Director at Greater Anglia, said: “This innovative collaboration integrates the data from forward-facing cameras on our trains to deliver valuable intelligence for colleagues leading vegetation management at Network Rail. Removing a reliance on old technology and potentially fallible human inspections/assessments, along with expensive cab riding and CCTV footage analysis, it is helping to deliver better infrastructure maintenance management and operational performance, through a pioneering partnership.”
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