Thursday, 13 February 2020

THERMAL IMAGING FOR SAFER AUTONOMOUS VEHICLES

For the automotive industry, pedestrian safety has been a serious concern since the horseless carriage. Londoner Arthur Edsall was the first driver to strike and kill a pedestrian in 1896 at a speed of four miles per hour. It took the U.S. Congress almost seventy years to impose automotive safety standards and mandate the installation of safety equipment and another thirty years before airbags became a required safety feature. Automotive safety standards in the United States are promulgated by a process of reviewing accidents after they have occurred.

Credits : pexels.com

In 2019, the National Transportation Safety Board (“NTSB”) finally addressed this standards - promulgation process in their Most Wanted List of transportation safety improvements calling for an increase in the implementation of collision-avoidance systems in all new highway vehicles. The progression of this change in policy derived from the 2015 study (SIR-15/01) that described the benefits of forward-collision-avoidance systems and their ability to prevent thousands of accidents.

After that report was published, an agreement was reached with the National Highway Traffic Safety Administration (“NHTSA”) and the Insurance Institute for Highway Safety that would require compliance with the Automatic Emergency Braking standard (“AEB”) on all manufactured vehicles by 2022. However, the agreement did not identify the specific technology that would enable AEB, and the question remains whether such technology is readily available and economically viable for industry-wide adoption.

RAPIDLY IMPROVING SENSOR TECHNOLOGY


The pace of technology over the last thirty years has been astronomical, yet technology to make driving safer has not kept pace. A computer that not too long ago was the size of a garage now fits into the palm of your hand. Today driving should be safer than ever, but the reality is that without the implantation of available modern technologies, the uncertainties of the road will always be with us. According to the NHTSA, there were 37,461 traffic fatalities in 2016 in the United States. 

In 2015, there were a total of 6,243,000 passenger car accidents. 1 Globally, there is a fatality every twenty-five seconds and an injury every 1.25 seconds. In the United States there is a fatality every thirteen minutes and an injury every thirteen seconds. These statistics are mind blowing. Compared to recent events affecting the aviation industry, two Boeing 737 MAX 8 airplanes crashed killing 346 people, the same number of people that die as a result of automobile accidents every 144 minutes, and all Boeing 737 MAX 8 airplanes were grounded 

The cost for automotive accidents is high. According to the national safety counsel, in the United States, the annual cost of health care resulting from cigarette smoking is approximately $300 billion whereas the annual cost of health care for injuries arising from automobile accidents is roughly $415 billion.

Technology to protect automobile occupants has reduced the number of driver and passenger fatalities. However, the number of people who die as a result of an accident outside the automobile continue to climb at an alarming rate. Pedestrians are at the greatest risk, especially after dark. 

The NHTSA reports that in 2018, 6,227 pedestrians were killed in United States traffic accidents, with seventy-eight percent of pedestrian deaths occurring at dusk, dawn, or night.2 In the United States, pedestrian fatalities have increased forty-one percent since 2008. Solutions to address pedestrian fatalities are needed to meet the standards by 2022.

TECHNOLOGY IN THE DRIVER’S SEAT


Ultimately, it is safer cars and safer drivers that make driving safer, and automotive designers need to deploy every possible technological tool to improve driver awareness and make cars more automatically responsive to impending risks. Today’s safest cars can be equipped with a multitude of cameras and sensors to make them hyper-sensitive to the world around them and intelligent enough to take safe evasive action as needed. Microprocessors can process images and identify subject matter 1,000,000 times faster than a human being

Advanced Driver Assist Systems (“ADAS”) are becoming the norm, spotting potential problems ahead of the automobile making auto travel safer for drivers, passengers, and pedestrians, not to mention the more than one million ‘reported’ animals struck by automobiles in the United States annually resulting in $4.2 billion in insurance claims each year. The advances we have seen so far are the first steps to evolving towards a future of truly autonomous vehicles that will revolutionize both personal and commercial transportation. 

Drivers need no longer rely on eyes alone to maintain situational awareness. Early generations of vision-assisting cameras were innovative, but they were not particularly intelligent and could do little to perceive the environment around the car and communicate information that could be used for driver decision-making.

Today, with tools such as radar, light detection and ranging (“LIDAR”), cameras, and ultrasound installed, a car knows much more about the environment than the driver does and can control the vehicle faster and safer than the human driver. Risky driving conditions such as rain, fog, snow, and glare, are less hazardous when a driver is assisted by additional onboard sensors and data processors.

One of the most advanced automotive sensors is a thermal sensor that allows a driver and the automobile to perceive the heat signature of anything ahead of the driver. Previously used mainly for military and commercial applications, early forms of night vision first came to the mainstream automotive market in the 2000 Cadillac DeVille, albeit as a cost-prohibitive accessory priced at almost at a cost approaching $3,000.

Since then, thermal cameras and sensors have become smaller, lighter, faster and cheaper. After years of exclusive availability in luxury models, thermal sensors are now ready to take their place among other automotive sensors to provide a first line of driving defense that reaches far beyond the reach of headlights in all vehicles, regardless of the cost of the vehicle


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Friday, 31 January 2020

THREE TRENDS DRIVING INDUSTRIAL AUTOMATION


Since its inception in the 1980s, machine vision has concerned itself with two things:improving the technology’s power and capability and making it easier to use. Today, machine vision is turning to higher-resolution cameras with greater intelligence to empower new automated solutions both on and off the plant floor — all with a simplicity of operation approaching that of the smartphone, which significantly reduces engineering requirements and associated costs.

And, just like in other industries which are benefiting from rapid advancements in technology like big data, the cloud, artificial intelligence (AI), and mobile, so too will manufacturers, logistics operations, and other enterprises benefit from three key advances in machine vision for automation.


RAPIDLY IMPROVING SENSOR TECHNOLOGY


While 1-, 2-, and 5-megapixel (MP) cameras continue to make up the bulk of machine vision camera shipments, we’re seeing considerable interest in even higher-resolution smart cameras, up to 12 MP. High-resolution sensors mean that a single smart camera inspecting an automobile engine can do the work of several lower resolution smart cameras while maintaining high-accuracy inspections.

Cognex’s patent-pending High Dynamic Range Plus (HDR+) image processing technology provides even better image fidelity than your typical HDR. It will help smart cameras inspect multiple areas across large objects where lighting uniformity is less than ideal. In the past, lighting variations could be mistaken for defects or the feature was not even visible. Today, HDR+ helps reduce the effects of lighting variations, enabling applications in challenging environments that were beyond the capability of machine vision technology just a few years ago.

While advanced smart cameras run HDR+ technology on field-programmable gate arrays (FPGAs) to improve the quality of the acquired image at frame rate speeds, complementary sensor technology, such as time-of-flight (ToF) sensors, are being incorporated to enable “distance-based dynamic focus”. The new high-powered integrated torch (HPIT ) image formation system, using ToF distance measurement and high-speed liquid lens technology, are also making an impact by enabling dynamic autofocus at frame rate.

The newest barcode readers incorporate HPIT capability for applications such as high-speed tunnel sortation and warehouse management in situations where packages and product size can vary significantly, requiring the camera to quickly adapt to different focal ranges.

INTEGRATION WITH DEEP LEARNING


Just like AI’s impact in other industries, deep learning vision software for factory automation is allowing enterprises to automate inspections that were previously only able to do manually or more efficiently solve complex inspection challenges that are cumbersome or time-consuming to do with traditional rule-based machine vision.

The biggest use driving the investment in deep learning is the potential of re-allocating, in many cases, hundreds of human inspectors with deep learning-based inspection systems. For the first time, manufacturers have a technology that offers an inspection solution that can achieve comparable performance to that of a human.

One example of how deep learning will benefit organizations is in defect detection inspection. Every manufacturer wants to eliminate industrial defects as much as possible and as early as possible in the manufacturing process to reduce downstream impacts that cost time and money.

Defect detection is challenging because it is nearly impossible to account for the sheer amount of variation in what constitutes a defect or what anomalies might fall within the range of acceptable variation. As a result, many manufacturers utilize human inspectors at the end of the process to perform a final check for unacceptable product defects. With deep learning, quality engineers can train a machine vision system to learn what is an acceptable or unacceptable defect from a data set of reference pictures rather than program the vision system to account for the thousands of defect possibilities.

THE INTERNET OF THINGS


An important development for smart camera vision systems enabling Industry 4.0 initiatives is Open Platform Communications Unified Architecture (OPC UA). With contributions from all major machine vision trade associations around the world, OPC UA is an industrial interoperability standard developed to help machine-to-machine communication.

Combined with advanced sensor technology and trends such as deep learning, OPC UA will help transition machine vision technology from a point solution to bridge the industrial world inside the plant and the physical world outside it. Today, vision systems and barcode readers are key sources of data for modern enterprises.

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WHAT ARE THE BENEFITS OF CMOS BASED MACHINE VISION CAMERAS VS CCD?




Industrial machine vision cameras historically have used CCD image sensors, but there is a transition in the industrial imaging marketplace to move to CMOS imagers. Why is this?.. Sony who is the primary supplier of image sensors announced in 2015 it will stop making CCD image sensors and is already past its last time buy. The market was nervous at first until we experienced the new CMOS image sensor designs. The latest Sony Pregius Image sensors provide increased performance with lower cost making it compelling to make changes to systems using older CCD image sensors.


WHAT IS THE DIFFERENCE BETWEEN CCD AND CMOS IMAGE SENSORS IN MACHINE VISION CAMERAS?


Both produce an image by taking light energy (photons) and convert them into an electrical charge, but the process is done very differently.
In CCD image sensors, each pixel collects light, but then is moved across the circuit via current through vertical and horizontal shift registers. The light level is then sampled in the read out circuitry. Essentially its a bucket brigade to move the pixel information around which takes time and power. In CMOS sensors, each pixel has the read out circuitry located at the photosensitive site. The analog to digital circuit samples the information very quickly and eliminates artifacts such as smear and blooming. The pixel architecture has also radically changed moving the photosensitive electronics to be more efficient in collecting light.


Courtesy of Automated Imaging Association

6 ADVANTAGES OF CMOS IMAGE SENSORS VS CCD


    There are many advantages of CMOS versus CCDs and outlined below: 

  • 1 – Higher Sensitivity due to the latest pixel architecture which is beneficial in lower light applications.

  • 2 – Lower dark noise will contribute to a higher fidelity image.

  • 3 – Pixel well depth (saturation capacity) is improved providing higher dynamic range.

  • 4 – Lower Power consumption. This becomes important as lower heat dissipation equals a cooler camera and less noise.

  • 5 – Lower cost! – 5 Megapixel cameras used to cost ~ $2500 and only achieve 15 fps and now cost ~ $450 with increased frame rates.

  • 6 – Smaller pixels reduce the sensor format decreasing the lens cost.

WHAT CMOS IMAGE SENSORS CROSS OVER FROM EXISTING CCD IMAGE SENSORS?

MVRPL can help in the transition starting with crossing over CCDs to CMOS using the following cross reference chart. Once identified, use the camera selector and select the sensor from the pull down menu.
CCD to CMOS cross reference chart

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HOW CAN A SYSTEM INTEGRATOR SUPPORT ENVIRONMENTAL SUSTAINABILITY?



Sustainability is on the mind of many companies, but are they using one of their most valuable assets? A system integrator can possibly be the missing link to making your organization green.

I will not write about the effects of the climate change, because we all know how critical the situation is. The attention is on how everyone should contribute on reducing the environmental impact of our activities. We all are focused on recycling, reducing use of plastic, avoiding wastes, and keeping transportation as ecological as possible.

BUT HOW CAN A COMPANY BE MORE AND MORE SUSTAINABLE, AND HOW CAN A SYSTEM INTEGRATOR SUPPORT THESE INITIATIVES?


The Intergovernmental Panel on Climate Change (IPCC)—the United Nations body for assessing the science related to climate change—states in its 2019 Special Report 

“Global Warming of 1.5 °C,” which focused on how the CO2 emissions reduction can help containing the global warming, that, “The industry sector is the largest end-use sector, both in terms of final energy demand and GHG [greenhouse gas] emissions. Its direct CO2 emissions currently account for about 25% of total energy-related and process CO2 emissions, and emissions have increased at an average annual rate of 3.4% between 2000 and 2014, significantly faster than total CO2 emissions”.

Most emissions are due to the combustion of fossil fuels, non-energy uses of fossil fuels in the petrochemical industry, and metal smelting—but transportation and electricity production also contribute.

In addition to emissions reduction, there are a lot of other guidelines to follow in order to have less of an impact on the environment. The four areas a company can intervene in to improve sustainability are:
  • Business: At the higher level, a sustainability strategy has to be implemented. Tradeoffs and priorities should be evaluated in order to obtain the prefixed goals. For example, when planning new constructions, the location of the plants has to be considered, so natural resource can be leveraged in order to have less of an impact the on environment and an early optimization of the transportation that will be later required. The upgrade of existing facilities should also improve the infrastructure.

  • Supply chain: The supply chain can be designed to optimize the network together with the route carbon. Policies regarding supplier packaging can be introduced and the right tradeoffs between just-in-time (JIT) and emissions should be evaluated. Transport, in some cases, can be zeroed using addictive manufacturing—where a supplier sends a file to be 3D printed instead of a good. This is a complete digital process with no impact on emissions production.

  • Design and engineering: When designing a product, there are many factors to be considered which will help the company’s journey through sustainability. Energy efficiency, carbon footprint, usage of alternative materials, and an energy bill of materials (BOM) can integrate the product BOM. This could be optimized during design and also the good packaging and its following disposal (both the good and the packaging).

    When engineering the process, some of the goals should be reducing asset energy, water, carbon, and waste burdens and improving flexibility.

  • Operations: On the operation side, the scheduling of production and the detail scheduling are fundamental instruments along with energy, water, and waste management.

    Production efficiency is one of the keys of sustainability and can be achieved only by integrating an efficient production process with asset energy monitoring and maintenance.

    Integrating an overall equipment effectiveness (OEE) calculation system can help optimize efficiency. The higher the efficiency, the lesser the wastes and consumptions.


  • SEVERAL BIG COMPANIES THAT ALREADY STARTED A SUSTAINABLE JOURNEY:

    • · More than 10 years ago, Walmart begun its journey. In 2017, they started Project Gigaton, involving its supplier with the goal of avoiding the production of a gigaton of greenhouse gases. All the goals a supplier sets itself have to be SMART (Specific, Measurable, Achievable, Relevant, Time limited), and for each goal reached, a supplier gains “credit”.

    • · Google has the goal of operating entirely using renewable energy. Google is also requiring their supply chain to be sustainable, improving its energy performance and scaling the deployment of renewable energy sources.

    • · Amazon is committed to meet the Paris Agreement 10 years early by using 100% renewable energy by 2030. It is also raising awareness among suppliers by requiring them to use proper packaging to reduce packaging waste throughout the supply chain.
    There are many other examples of smaller companies in pursuit of sustainability. The energy consumption is often monitored and measured in order to optimize its use of water consumption and waste production. Many other initiatives are rolled out in order to influence people behavior, where a lot of companies are promoting reuse of materials reducing the use of plastic (removing plastic glasses and providing water bottles to the employees, for example)

    Others are encouraging smart-working to reduce the CO2 emissions related to home-to-work transportation. We can probably say that most of the companies have already implemented some kind of sustainability program.

    For many years, system integrators have been involved in several traditional efficiency improvement activities like motor replacement, inverters installation, and many others strictly related to energy usage reduction. But there’s much more a system integrator can do to help a company reduce its environmental impact through the operations optimization.

    Connecting information coming from the production plant and crossing them with energy consumption information and point-of-sale (POS) details can provide reports that can help identify possible optimizations, where having all the information separately couldn’t have been enlightening. Implementing OEE systems in productions lines helps increasing efficiency and availability. Scheduling can also be improved thanks to the data collected from the field. These are only examples of benefits that can be achieved with an interconnected system, and there are many more examples that could be done.

    Regulations are probably changing to make companies accountable for every occasion they aren’t “green.” Can you imagine the future of your company without a sustainability project?

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