Computer Vision in Transportation


Computer Vision is an aspect of artificial intelligence that is important in the ability of systems to observe and gather information from visual media. This is used across industries where visualization and inspection of objects and surroundings are necessary. To achieve this, a lot of data is analysed using machine learning and convoluted neural networks. The aim of this is to enable the system to learn from repeated observation of images. These images are broken down by the CNNs to make possible predictions which are processed by the systems and used in decision-making. It also powers advanced driver assistance systems that contribute to safety of driving. Computer Vision is quickly becoming an important part of several industries with an estimated market value of $48.6bn. As automation takes hold in the transportation industry due to its promise of sustainability and efficient operations, computer vision will become a necessary addition to systems. More companies and governments are thereby looking to design and create programs for a wide range of uses in transportation.

Computer Vision and Transportation 

Computer Vision is especially useful in the transportation industry where there is a multitude of visual information to be collected and processed. As automation becomes a priority for operators, different fields of AI will be required to provide more efficient and advanced systems. 

Security: Safety and security features can be implemented using systems powered by computer vision to capture and access visual information. This is especially important in the context of developing self-driving vehicles which will have to assess dangerous situations and react as quickly as the human mind to safety threats.

Passenger Identity Confirmation: As stations and airports alike began to explore self check-in, automatic confirmation of passenger identity and proper seat placement can be ensured using computer vision to correctly identify and match passengers to their seat. 

Maintenance: Cameras can be employed in inspection procedures to quickly identify faults in infrastructure and alert operators to the need for adjustments and repairs. Frequent inspections will also contribute to the pool of available information and help to improve the accuracy of the systems overtime.

Accident Prevention: Several millions of people worldwide are victims of road traffic accidents annually. To avoid this, high powered sensors on cars and other vehicles can collect information and notify drivers when they’re too close to pedestrians and other motorists. Even further, these systems can connect to the operations of the vehicles and provide suggestions for adjustments in speed and direction to avoid collisions. Lane departure warning systems can also be used in combination with cameras mounted on vehicles to notify drivers when they risk approaching lane markings without turn signals.

Traffic Management Systems: As more cities begin to go digital, even traffic management systems can be integrated with automation to provide drivers with dynamic information regarding which routes are better and more accessible to them. Traffic lights and smart parking systems will benefit from computer vision to ensure that updated information is provided to motorists and other road operators at all times. Computer vision can also be applied in monitoring vehicles for the purpose of determining adherence to road traffic rules, speed limit etc.

Detecting Unidentified Packages: Anomalies in surroundings such as parked vehicles or even unattended luggage at stations and airports can be spotted and quickly analyzed to determine if they pose a threat to other travelers. This can then be appropriately checked to ensure safety of the environment.

Challenges in the Implementation of Computer Vision

A few challenges may, however, affect the implementation of computer vision in transportation systems. One of such challenges is its adaptation to real life scenarios with conditions that may vary greatly from testing conditions. In order to overcome this, algorithms must display accuracy in evaluating a wide range of weather conditions, terrain and other factors peculiar to location. In addition to this, cost effective methods will be required to integrate these new technologies into existing road infrastructure in a way that benefits road users, governments and other involved parties. 

Further innovations in transportation will continue to drive the need for better visual recognition systems. Computer vision presents an opportunity to ensure safety and efficiency of automated transportation. It also ensures that governments and companies are better able to manage revenue by reducing losses due to delays and inspections, all of which can be made faster and more efficient. Undoubtedly, this will create new and exciting ways for customers to travel with sustainability while also maintaining revenue and service delivery in the transportation industry.


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