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Visual AI Railway Inspections: Better Detection of Railroad Defects and Obstacles

Railway operators must conduct routine inspections and maintenance of tracks, trains, and other equipment to ensure the safe operation of railways. Through these inspection and maintenance activities, railway operators prevent service interruptions and, most importantly, reduce the chances of catastrophic railway accidents by resolving some of the most common causes of accidents, such as train and equipment failures, track defects, and other issues.

While trains require more maintenance than any other piece of railroad infrastructure, tracks are also highly prone to causing breakdowns and delays when neglected. Ultimately, the successful completion of detailed visual inspections of trains, tracks, and other equipment is the first line of defense against neglected maintenance issues leading to accidents.

Beyond routine maintenance inspections and repairs, train conductors and engineers help prevent accidents by constantly watching for obstacles – such as vehicles, rocks, trees, livestock, and people – on the tracks ahead. If train drivers detect these obstacles early enough, there’s a better chance of avoiding a disastrous accident.

The latest computer vision technology offers railway operators tremendous ROI benefits in terms of earlier and more affordable detection of defects and obstacles. In fact, computer vision offers dramatic efficiency improvements over traditional methods of defect and obstacle detection.

Traditional Methods of Railway Defect and Obstacle Detection

Traditional methods for detecting rail- and train-related flaws and maintenance issues include visual inspection, ultrasound, liquid penetration inspection (LPI), radiography, and more. Visual inspections are particularly costly and inconvenient to perform, as they require teams of highly trained technicians to walk along tracks and trains to look for problems in need of repair. Railways also use cameras to assist with the inspection process.

During rail and train inspections, human technicians must visually evaluate the condition of rails, ties, track ballast, mounting systems, train wheels, train undercarriages, and other details. Human errors and oversights abound in this process, and it’s not uncommon for inspectors to accidentally overlook a glaring maintenance concern that needs immediate repair to prevent train derailment or service shutdowns. As discussed in further detail below, railway operators incur massive costs when these defects go unnoticed.

As for obstacle detection, conductors and engineers must rely on their keen eyesight and focus. Unfortunately, it usually doesn’t matter how early conductors can visually identify an obstacle. Trains typically cannot stop quickly enough to avoid a collision.

The Challenges of Visual Defect and Obstacle Detection

Despite spending millions of dollars each year to inspect North American railroads, railway inspection processes are fraught with problems and errors. This is mostly the result of:

  • Staff shortages: Cost cuts and a lack of skilled inspectors mean that some railway operators may not have enough inspectors on hand to monitor all track assets with sufficient regularity and attention to detail.
  • Poor management decisions surrounding inspections: Railway industry managers are prone to making mistakes when it comes to balancing the limited resources they can direct toward track inspection and maintenance. This can result in the neglect of track and train assets that need more attention and care.
  • Inadequately performed inspections: Human railway inspectors are prone to missing details and making mistakes as a result of strict time constraints, distraction, fatigue, and the limitations of human capacity.
  • A lack of regular inspections: Some railway operators must divert their limited inspection resources to key pieces of infrastructure. This can lead to infrequent or inadequate inspections of less essential sections of track.
  • Human limitation: Train engineers are limited by how far ahead they can see. Plus, a curving track, trees, and buildings could obscure upcoming obstacles. This makes it difficult to detect livestock, people, and other obstacles early enough to stop the train to avert a collision. Train track suicides are also common. Tragically, many engineers remember the times they weren’t able to stop the train in time to prevent someone from dying.

The Cost of Railway Inspection Errors and Train Accidents

Failure to detect maintenance issues results in railroad operators finding out about problems too late – and the costs can be catastrophic. In these cases, an easily fixable defect can turn into a problem that’s expensive to repair or results in a serious accident.

According to the most recent data from the U.S. Department of Transportation Federal Railroad Administration, human error, track failures, miscellaneous factors (such as collisions with obstacles, animals, and people), and equipment failures cause the majority of train accidents.

Train Accident Chart

Some of the costs associated with failing to detect railroad defects and obstacles include:

  • Replacing and repairing train equipment and railroad tracks.
  • Higher repair and maintenance costs.
  • Personal injuries and wrongful death liabilities.
  • Damage to goods and supplies the train was transporting.
  • Lost customers and fewer sales from reputation damage and service delays.
  • Environmental impact and cleanup of hazardous material spills.
  • Psychological and emotional turmoil experienced by train drivers after witnessing a human death.

Leveraging Visual AI for Better Railway Defect and Obstacle Detection

Visual AI technology can offer a cost-effective and highly efficient solution for detecting defects and obstacles earlier, more accurately, and dramatically more affordably than railway operators can achieve with human inspectors and train engineers alone.
Computer vision strategies for railroad defect and obstacle detection leverage the following features:

  • High-definition cameras mounted on the undercarriages, fronts, and sides of railway cars and along railway tracks.
  • Servers running sophisticated AI models that interpret visual data.
  • Infrared and high-definition cameras that scan railway tracks for obstacles like animals, rocks, trees, people, vehicles, and debris.
  • Instant reports and alerts sent to decision-makers who can immediately trigger a repair request for further investigation.
  • Instant reports and alerts sent to train conductors and engineers who can slow down or stop trains as early as possible to prevent collisions.

With Chooch AI computer vision technology, railway operators can rapidly train visual AI models to detect all types of visually perceivable defects and objects. In fact, Chooch AI can develop, train, and implement a custom visual AI strategy in only six to nine days. In this short amount of time, railway operators can start to realize the tremendous ROI benefits that come from faster, more accurate, and more affordable railway defect and obstacle detection.

In summary, Chooch AI computer vision models for the railroad industry can help railway operators:

  • Detect obstacles in the path of trains earlier than human engineers to provide additional time to prevent collisions and suicides.
  • Visually detect maintenance issues on trains, wheels, and undercarriages.
  • Visually identify track defects related to welds, cracks, and other maintenance issues.
  • Evaluate the conditions of railway ties, track ballast, and mounting systems.
  • Reduce train accidents and associated costs and damages.
  • Reduce the cost of track, train, and equipment inspections.
  • Reduce the cost of track, train, and equipment maintenance through earlier detection of defects and problems.