Computer vision applications are vast and varied, impacting industries ranging from manufacturing and automotive to energy and utilities. The market for computer vision is expanding at an unprecedented pace. In fact, it’s expected to show an annual growth rate (CAGR 2024-2030) of 10.50%, resulting in a market volume of US$46.96bn by 2030. This article explores the role of computer vision in improving quality control for field operations, particularly in the telecom sector, and its impact on other industries.
Computer vision systems provide real-time feedback on work quality when analysing visual data, whether in telecommunications, manufacturing, construction, or utility inspections, allowing teams to address issues promptly and maintain high standards. This capability greatly reduces site visits as tasks are verified on the first attempt, saving time and resources. Moreover, computer vision automates inspections and monitoring processes in industries where regulatory compliance is crucial – like utilities and transportation.
Computer vision is a branch of Artificial Intelligence (AI) that empowers machines to interpret and understand visual data. It employs machine learning and neural networks to enable computers to extract meaningful information from digital images, videos, and other visual inputs.
In essence, if AI equips computers with the capacity to think, computer vision allows them to see, observe and comprehend. It aims to replicate and improve the human visual system's ability to perceive and make sense of the surrounding environment through images and videos.
Computer vision involves developing algorithms and techniques to extract meaningful insights, identify patterns, recognise objects or scenes, and make decisions based on visual data.
Computer vision is becoming more integrated in the telecommunications industry to enhance aspects of communication and services. One key example is in video conferencing platforms, where computer vision algorithms improve video quality, optimise usage, and enable features like background blur or virtual backgrounds.
Moreover, computer vision is utilised for network monitoring and upkeep tasks like identifying and analysing infrastructure issues such as cable damage or equipment malfunctions. In telecoms, the fusion of computer vision, AI, and data analytics fosters advancements that elevate user experience, network efficiency, and overall service dependability.
Additional examples include:
As part of the utilities sector, computer vision is crucial for monitoring and managing critical infrastructure. It enables companies to conduct efficient inspections of power lines, substations, and pipelines using drones or cameras equipped with advanced imaging capabilities.
Using computer vision systems, utility services can detect anomalies, identify faults, and prioritise maintenance activities, improving reliability and resilience. Additionally, computer vision optimises grid management, automates meter reading processes, improves safety and security measures, and drives overall operational efficiency within the utility sector.
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Construction is beginning see significant benefits with the introduction of computer vision. It's used in automated machinery which can identify and sort materials, operate in hazardous areas, and perform repetitive tasks more efficiently than human workers.
Other areas include monitoring construction sites, identifying potential safety hazards and ensuring that work is being carried out according to plans. Furthermore, it aids in surveying and mapping construction sites, providing precise measurements and detailed site analysis. In essence, computer vision is becoming an indispensable tool in the construction industry for boosting productivity while ensuring safety.
Use case examples:
Computer vision technology is used to automatically recognise Electric Vehicles (EVs), helping to monitor the charging process and even manage the parking spaces around charging stations.
Advanced algorithms can identify the type of vehicle and its charging port type and execute an automated charging process. This improves the efficiency of EV charging stations and enhances the customer experience by reducing human errors and providing a seamless charging experience. Hence, computer vision significantly contributes to the growth and sophistication of EV charging infrastructure.
Use case examples:
Axione is a leading telecommunications infrastructure provider in France, specialising in the design, construction, and operation of digital networks. With a focus on delivering high-performance solutions, Axione supports the connectivity needs of businesses, government agencies, and communities across various sectors.
The company's expertise spans fibre optic networks, wireless communication systems, and innovative technologies, enabling reliable and efficient connectivity solutions tailored to the specific requirements of its clients.
Axione oversees more than 6M FTTH premises and 25 public initiative networks, connecting over 175 municipalities. However, despite being experts in addressing the complexities of nationwide cabling and wiring, the manual reviewing process proved to be extremely time-consuming and inefficient.
However, once we partnered up, the situation quickly changed, and we were able to reach amazing results:
