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Round 2 Challenges and Successful Startups.

Detecting Safety Risks in Critical Infrastructure Industries.

Round 2 Overview: Eleven startups were shortlisted to pitch for our second innovation day, in areas such as AI, computer vision and behaviour recognition. Cutting-edge solutions were presented, to solve some of the most urgent safety challenges within the marine, energy and critical infrastructure industries, set by the Safety Accelerator in collaboration with challenge partners ScorpioPacific International Lines (PIL)Infratek and Odfjell Drilling.

 

Challenge: Determining master’s and crew’s psychological and emotional wellbeing.

Set in conjunction with Pacific International Lines (PIL).

Marine jobs are some of the most stressful in the world and the psychological and emotional factors not only affect the safety and health of the concerned seafarer, but also the immediate safety of others onboard, safety of the vessels and the marine environment. Currently there is a lack of feasible means to determine the psychological and emotional status of a seafarer before he or she is assigned to an important task or scheduled watches, which would likely lead to injury, death or damage to property or environment if the seafarer is not psychologically sound.

The challenge set in partnership with Pacific International Lines (PIL), seeks innovative methods to assess the psychological and emotional wellbeing of individual crew members in real-time, whilst they are onboard and before they are about to go on duty, whilst protecting user privacy.

Finalists:

Emotion Research Lab allows machines to understand human emotions through affective computing (emotional AI). Emotion Research Lab’s work provides a global vision, quantitative and qualitative, of emotions. It reveals the direction and typology over facial expressions video analysis. There are different applications according to the sector which you would apply.

Senseye is building a direct link between humans and computers, and working to enable you to use the human brain tomorrow, the way a mouse and keyboard are used today. Senseye’s moonshot, Human-Computer Symbiosis, will revolutionize the way humans communicate with technology, using a unique, sensory interface technology, we are developing a direct link from the brain to a computer via the eye.

Aveling uses bio-markers and wearable tech to understand people’s emotional and psychological state.

Stroma is developing a vision based driver and worker state monitoring device. Stroma is an intelligent driver-facing technology for the automotive industry. Cars equipped with Advanced Driver State Monitoring System understand driver behavior, allowing drivers to make better decisions and improve comfort and safety. Sophisticated image processing algorithms learn to track driver attention, fatigue and emotions utilizing physiological models. Realtime biometrical readings can detect drunken driving to drastically improve road safety.

Winner: Senseye

 

Challenge: Improving how quickly errors are spotted in work on infrastructure assets.

Set in conjunction with Infratek.

Currently the process infrastructure builders and operators use to document work done on an asset is quite manual and retrospective, requiring a fitter to use a mobile device take photographs of their daily work on a site, a case handler to review and spot check the reported photos and flag errors such as jobs left incomplete or not done correctly, and a worker to go out to “fix” the potentially hazardous errors.

This challenge, set in conjunction with Infratek, seeks innovative proposals for solutions that can harness the capabilities of computer vision, artificial intelligence and machine learning to not only improve how quickly errors are spotted in work on infrastructure assets, but to go further to offer real-time detection and feedback to the worker prior to leaving site to ensure the job is done right the first time, and improve safety for the worker and third parties.

Finalists:

Cogniac enables enterprise customers to extract information from ever-increasing streams of video and image data. If you can see an item or condition of interest — even if it requires professional expertise — Cogniac’s system can automatically observe images and video and detect that item. Using modern advances in deep learning, Cogniac’s products achieve better-than-human performance at many highly-focused observation tasks.

Numberboost applies the use of machine learning to solve computer vision problems through custom interactive visualisations and data analytics. NumberBoost builds custom solutions using cutting-edge machine learning algorithms to solve a variety of real-world prediction and computer perception problems. NumberBoost has experience building custom interactive visualizations and handling massive datasets using high-performance distributed computing technologies.

SmartVid.io is a platform that uses machine learning to help you gather, find and use your industrial photos and videos. At Smartvid.io they’re united by a common goal—building a photo and video management platform that leverages machine learning to solve industrial-grade problems. Smartvid.io’s vision is simple to explain but hard to do. To execute on this concept, they’ve convened an experienced team of enterprise software engineers, machine learning specialists, and account managers.

Winner: Numberboost

 

Challenge: Video detection of physical asset changes to prevent falling objects.

Set in conjunction with Odfjell Drilling.

Oil and gas drilling infrastructure generally requires manual, calendar-based inspections and frequent scrutiny by workers to detect insecure objects and changes to the structural parts of a rig and equipment.

This challenge, set in conjunction with Odfjell Drilling is seeking innovative solutions using computer vision and image processing to analyse drilling sites’ existing closed-circuit camera network to enable operators to detect significant changes to an oil drilling rig infrastructure that could result in falling objects injuring workers and damaging the asset.

Finalists:

Cogniac enables enterprise customers to extract information from ever-increasing streams of video and image data. If you can see an item or condition of interest — even if it requires professional expertise — Cogniac’s system can automatically observe images and video and detect that item. Using modern advances in deep learning, Cogniac’s products achieve better-than-human performance at many highly-focused observation tasks.

SmartVid.io is a platform that uses machine learning to help you gather, find and use your industrial photos and videos. At Smartvid.io they’re united by a common goal—building a photo and video management platform that leverages machine learning to solve industrial-grade problems. Smartvid.io’s vision is simple to explain but hard to do. To execute on this concept, they’ve convened an experienced team of enterprise software engineers, machine learning specialists, and account managers.

InstaDeep uses deep reinforcement learning to create AI systems that makes decisions in industrial environments. InstaDeep delivers Artificial Intelligence products for the Enterprise. InstaDeep’s team harnesses the power of deep reinforcement learning to create AI systems that can make decisions in real-life industrial environments. By combining the skills of AI researchers, ML engineers, Hardware and Visualization experts, InstaDeep builds end-to-end products that can tackle the most challenging optimization and automation challenges.

Winner: Cogniac

 

Challenge: Understanding why wrong decisions happen to reduce the risk of human error incidents.

Set in conjunction with Scorpio.

Human error remains the most important factor in marine accidents; in spite of having known better or having the necessary training and competency, seafarers can still make decisions which lead to undesired outcomes, injury or damage to assets. This challenge, set in conjunction with Scorpio, seeks to help ship operators gather data about and analyse the behaviours of crew and the onboard work environment, whilst safeguarding user privacy, to develop a deeper understanding of why wrong decisions happen and ultimately to improve processes, training and staffing to reduce risk of human error incidents.

Solutions should use and augment existing ship data including voice data feeds and historic incident voyage data to help process and analyse what actually happened that led to an undesired outcome, uncover the relationship between discovered patterns and undesired outcomes, and gain real time insights rather than learn only from post mortems following an incident.

Finalists:

Hala Systems is a for-profit social enterprise focused on technology-driven solutions to problems related to civilian security and safety. Hala Systems develops advanced solutions for civilian and asset protection, accountability, and the prevention of violence before, during, and after conflict. Through the development and implementation of innovative technology, we aim to reduce harm, increase security, and stabilize communities.

Contiamo has developed a platform enabling corporates to utilise the existing data they have and build AI (Machine Learning Models) on top of their existing IT infrastructure. The company’s combination of a highly-qualified team, an industry-leading platform, and vast experience working on data science projects uniquely positions Contiamo to partner with industry giants, enabling them to achieve their goals. Contiamo has a proven track record of achieving tangible business value with companies, such as Deutsche Telekom, through the delivery of major transformational projects.

fuseAware utilises IOT, Machine Learning and wider AI technologies to assess and improve health, safety and productivity in real time. fuseAware is a SaaS platform providing real time workplace intelligence across health, well being, safety and productivity for mobile workers. fuseAware’s goal is to deliver significant productivity gains to our clients while also delivering best in market safety, health and well-being benefits to workers.

Winner: To be confirmed.

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