TL;DR:
- Niricson Software leverages machine learning to enhance efficiency and cost-effectiveness in infrastructure inspections.
- Machine learning provides unbiased analysis, identifying and quantifying defects in a non-biased manner.
- Automation facilitated by machine learning streamlines drone-based inspections, enabling faster and higher-quality assessments.
- Niricson’s expertise spans drone-based data collection and inspection projects in various regions.
- The company focuses on using machine learning and AI to detect and quantify defects in concrete assets.
- Niricson’s proprietary acoustic sounding payload enables accurate identification of concrete delamination.
- Coordination with drone pilots and strategic partnerships ensures global reach and high-quality data collection.
- Trustworthy and qualified pilots are crucial for capturing high-resolution imagery and executing intricate flight plans.
- Niricson’s extensive experience and data-driven approach empower accurate change detection over time.
- The company’s growth trajectory is driven by successful projects and partnerships with global clients.
Main AI News:
In the realm of infrastructure inspections, Niricson, a leading software company, is harnessing the power of machine learning to revolutionize traditional assessment processes. Daan Arscott, the Data Collection Lead at Niricson, highlights the efficiency and cost-effectiveness afforded by machine learning in comparison to conventional methods. With extensive experience overseeing projects involving hydro dams, bridges, tunnels, airfields, and other substantial concrete assets, Arscott has witnessed firsthand the profound impact of machine learning on drone-based infrastructure condition assessments, making them more accurate, efficient, and safe.
Arscott explains that machine learning possesses several key features that make it an invaluable tool for inspections. Firstly, it allows for an unbiased analysis of collected data. By scrutinizing the surface and leveraging high-quality imagery and resolution, machine learning can objectively identify and quantify defects, devoid of any subjective bias.
Moreover, machine learning drives automation in drone-based inspections, propelling them beyond mere digitization of assets. Arscott elucidates, “While having a 3D model of your structure is beneficial, it ultimately requires meticulous manual examination to identify all cracks and deficiencies for a comprehensive engineering assessment. Niricson automates this process, resulting in faster and higher-quality assessments.”
Over the past few years, Niricson has successfully executed drone-based data collection and inspection projects across various regions, including Canada, the United States, Australia, and New Zealand. Arscott emphasizes that Niricson prides itself on being a software company specializing in utilizing machine learning and AI to automatically detect and quantify defects on concrete assets. While their primary focus lies in software development, Niricson recognizes the criticality of high-quality data collection, as it significantly enhances the value and efficacy of their machine learning and AI solutions.
Niricson’s proprietary acoustic sounding payload assumes a pivotal role in their operations. By simulating both the eyes and ears of an engineer through drone-mounted cameras and in-house developed acoustic technology, Niricson can physically tap the structure, emulating the traditional concrete hammer test, and accurately identify concrete delamination.
As part of his responsibilities at Niricson, Arscott plays a vital role in coordinating the various systems and personnel involved in inspecting and monitoring these expansive assets. He acts as a liaison between the operations pipeline, responsible for data processing and uploading to the cloud platform, and the sales team engaged in business development and contracting.
Collaborating with drone pilots constitutes an essential aspect of Arscott’s work. By directly engaging with clients and hiring third-party pilots for data collection, Niricson maintains a global reach without the need to establish service teams worldwide. Leveraging strategic partnerships, Niricson ensures quality data collection and expeditious project execution. In certain cases, Niricson also provides support and training to clients’ internal drone teams, empowering asset owners to independently gather data using their drones, potentially transitioning Niricson into a software-only solution in the future.
Arscott underscores the criticality of recruiting “trusted, qualified pilots” for these projects. The acquisition of high-resolution imagery capable of capturing minute defects necessitates meticulous flight planning. Arscott personally engages with pilots, drawing upon trusted relationships, and spends time on-site to share knowledge, perform QA/QC work, and ensure the data’s quality before handing it over to the processing team. Subsequently, Niricson’s internal teams examine the 3D models, apply machine learning algorithms, and upload the AI-generated defect maps to their cloud platform.
With an impressive track record of over 50 drone-based data collection projects in the past three years, Niricson has established a comprehensive baseline for large infrastructure assets, complete with quantification data. This valuable resource enables them to revisit sites year after year, conducting full rescans of structures and facilitating comparisons between the two datasets to monitor changes over time. Accurate change detection emerges as a significant value-addition within their solution.
The company’s proficiency in utilizing advanced technologies has fueled its growth trajectory. Arscott confidently asserts, “2023 is shaping up to be a remarkable year for us.” Already having completed several projects across North America and wrapping up additional endeavors in Australia and New Zealand, Niricson is at the forefront of inspecting the world’s largest, most iconic, and highly complex infrastructure assets.
Conclusion:
Niricson’s utilization of machine learning in drone-based inspections brings significant advancements to the infrastructure market. The application of unbiased analysis, automation, and accurate defect identification leads to enhanced efficiency and cost-effectiveness. The ability to monitor changes over time and provide accurate change detection offers valuable insights for asset owners and stakeholders. As the company continues to expand its reach and deliver high-quality results, Niricson is poised to shape the future of infrastructure inspections.
