Each passing year, machine vision is finding its way into applications that were not commercially feasible — and in some cases, impossible — prior to new software advances such as deep learning and related neural network processing techniques. Despite being some 50 years along the maturation curve, machine vision is just beginning to see its full potential as it heads toward the next industrial revolution, marked by increased machine-to-machine cooperation and machine-to-human collaboration.
The growth in machine vision adoption, prompted in large part by the convergence of advanced imaging analysis, robotics, machine learning, and related automation, is leading to creative, never-before-seen solutions, to unique challenges inside and outside the factory. As the “eyes” of the system, machine vision cameras will remain critical to the development of next-level solutions in a variety of growing global markets.
Sensor Type: CCD, CMOS, and More
Since global shutters came to complementary metal oxide semiconductor (CMOS) optical sensors, the market has long predicted the demise of the charge-coupled detector (CCD) due to advantages of CMOS. It has a cheaper manufacturing process, higher speed operation, and ability to integrate microprocessors, memory, and other standard electronic blocks on chip (e.g., Xilinx's Xinq, Intel Movidius, Max10, and Cyclone). But like so many predictions, each new year brings renewed — and mistaken — reports of CCD's demise.
CCD maintains a strong position in life sciences and related applications because of its ability to reduce the depth of the silicon layer on the back of the sensor (back-thin) for greater optical sensitivity, lack of fixed pattern noise, and lower noise floor. However, CMOS has taken over the majority of the machine vision market. CCD sensors still offer fewer dead pixels compared with standard CMOS chip yields and higher quantum efficiency/responsivity, which is important for near infrared (NIR), scientific, and life science applications. These include microscopy and endoscopy applications, where sensitivity is critical to acquiring useful images.
Other types of sensors include microbolometer and InGaAs sensors in IR/thermal vision applications — a common technique used to monitor assets based on operating temperature in forges, metal working, and related applications.
Resolution: Area Arrays, Line Scan
Every few years, optical sensor yields improve enough to reduce costs to where machine vision customers can jump from one resolution plateau to the next. This holds true whether we are talking area arrays or linear arrays, i.e., line scan cameras. In general, more resolution offers one of two benefits: either more pixels to help identify a defect in an image (e.g., smaller defects on shrinking wafer traces), or the ability to do more inspections in a given field of view (e.g., large wafer/flat-panel inspections) — especially as even “dumb” component cameras start to add user-set multiple regions of interest to their available feature offerings.
In 2018, the most common component camera went from 2 to 5 megapixels (MP). In the next couple of years, camera OEMs are predicting a jump to 10 MP as the standard size of a machine vision sensor array. This is achievable while still maintaining realistic pixel sizes of several microns square so that the most commonly available optics still have the ability to resolve light on individual pixels. This is unlike the massive sensor arrays in smart phones with cheap optics that cannot deliver on the true resolution promise of 12+ MP sensors with pixels of 1.4 μm. Conversely, some of the largest machine vision cameras today for the semiconductor industry offer 50 MP with 5-μm pitch pixels, providing massive resolutions at viable pixel sizes for standard camera optics.
Within the line scan segment, 8k array lengths are the new norm, up from 4k line scan. Thanks to the ability to build just one, or only a few, rows of pixels, line scan cameras are able to maintain large pixel sizes for light sensitivity more easily while still running at high readout speeds. In some cases, they use multiple “taps” to read out sections of a long linear array sensor and time domain integration (TDI) to bin multiple rows of pixels into a single pixel value. These techniques allow new cameras to keep up with increasing production line speeds. Insiders say the next linear array plateau is likely to be 15 to 16k linear arrays, which will place additional requirements on the optics to capture as much light as possible across the full sensor length.
The challenge of matching optics with larger sensor sizes sporting smaller pixels will continue into the future among both area and linear array sensor groups. Camera experts say 3-μm pixels is the line at which developing effective, cost-efficient, optics becomes problematic, if not practically impossible, using today's optical manufacturing technology.
Speed: Area Arrays, Line Scan
Higher speed, usually defined by a camera's frame rate, allows customers to inspect more products per minute and/or help to freeze the motion of fast-moving objects while limiting blur. This improves the machine vision system's ability to make an accurate automated decision or measurement.
Today, 200 fps speeds for area arrays and 70 kHz frame rates for 4k linear arrays are fairly common, with maximum frame rates two to three times above those levels. Luckily for the camera interfaces, few customers operate machine vision cameras at 100% of full frame rate. Instead, common applications acquire several images in a very short period of time and then send the data to the processing unit in a burst, easing the bandwidth requirements on imaging networks.
GigE Vision can accommodate the majority of applications and the frame rates and data transmission bandwidth requirements of the associated cameras. I/O specialists in imaging network applications, however, say they are seeing interest in 2.5 Gpbs GigE Vision over NBASE-T as an intermediate step towards 10GigE. Today, consumer markets have yet to adopt 10GigE as the default port in desktops and laptops due to cost, power consumption, and thermal design challenges.
In general, machine vision camera OEMs of area and linear arrays believe that while special cases will always exist in which customers want cameras that run at higher speeds, most applications are adequately served by today's off-the-shelf camera offerings. If a bottleneck exists for a given application, it's usually in the network at this point. This necessitates a move toward a frame grabber–based protocol such as Camera Link High Speed, which has a higher throughput than standard GigE Vision and USB3 Vision.
Color Cameras and Lighting
Machine vision integrators estimate that despite regular growth of color machine vision cameras, 90% of machine vision applications are solved by a monochrome camera. Traditionally, the reason has been cost, as color cameras are more expensive than monochrome cameras with the same resolution. The cost-per pixel issue becomes more pronounced if the color camera uses the common Bayer filter, which reduces the camera's resolution to one-third of the original resolution.
For the most accurate color representation, 3CCD cameras have been the standard. These cameras use a prism to separate the colors, rather than filters applied directly to a single array. In this case, each sensor acquires one color channel, and the three separate images are combined in software to produce a more realistic representation of the object than is achievable by using Bayer filters. Additionally, this more costly approach does not come with a resolution penalty. For the most accurate print inspection applications, for example, prism-based chip line scan cameras have been the gold standard for years. This has been due to the accuracy of the color representation, the ability to limit spectral overlap between channels, and improved spatial alignment between color channels.
In the past year, the machine vision camera market has seen the first line scan camera to offer different polarization filters applied to different rows, a useful feature for flat-panel display inspection. Camera OEMs expect this trend toward multichannel, customized filtering to continue.
Lighting is also taking a more active role in machine vision solutions in general, but in color applications in particular. Cameras with multiple exposures and regions of interest can perform multiple inspections, many of which may require different illumination techniques. Some camera OEMs are using single-sensor cameras and time domain rather than optical filtering to separate color channels. This results in multispectral solutions with lower throughput and lower cost but requires more complex illumination sources and controls.
The most prevalent applications for machine vision cameras still use visible light. Certain applications, however, utilize less popular lighting methods, such as x-ray, ultraviolet (UV), near infrared (NIR), shortwave infrared (SWIR), and long-wave infrared (LWIR).
One of the most common applications for x-ray lighting is metallurgy inspection, which shows structural defects or fatigue in metal components. NIR lighting is used to present higher-contrast images in applications such as food inspection, where it becomes possible to identify nonorganic or dead products that may be present among fresh ones. Other agricultural inspection applications use NIR lighting to identify rotten fruit or vegetables. NIR is also used in waste management, recycling, and in the semiconductor industry. UV, SWIR, and LWIR are still very niche for machine vision applications.
Other Technology Developments and Trends
As robotic vision becomes more common and machine vision extends its support to applications beyond the plant floor, the need for accurate 3D machine vision increases. Today, designers have many different ways to solve a 3D vision application: stereovision using multiple cameras to detect 3D position based on slight changes in perspective; single-camera 3D, where images are acquired from different locations and use perspective processing similar to stereovision to determine 3D position; single camera, either area or line scan, with structured light projection; and static camera with multiple unstructured illumination sources positioned around the target (known as photometric 3D). Options abound and will only continue to grow.
In response, many machine vision camera makers now offer optimized 3D cameras with factory-calibrated measurement systems that greatly simplify the integration of 3D vision. Furthermore, GeniCam 3.0 has simplified the detection and programming of standard 3D camera solutions, removing one of the last remaining challenges to increased 3D camera market adoption.
Smart Cameras and Embedded Vision
Any discussion of embedded vision needs to start with a discussion about the future of smart cameras, because smart cameras represent the first volume application for embedded vision technology. Embedded machine vision systems combine all elements of a machine vision system, including image acquisition, image processing, and communication in a form factor that prizes compact size and low power consumption.
Previously, smart camera integration was done at the board level. These systems put all elements on as few boards as possible in a single enclosure. However, 2018 marked the first year that traditional machine vision companies were able to access new system-on-a-chip (SoC) solutions such as the Xilinx Xinq, which combines Arm cores and FPGA arrays on a single chip. The industry is still waiting on volume access to Intel's Movidius SoC chipset. It is safe to say that this new highly integrated vision SoC solution could significantly alter smart camera capability, performance and cost. It will open up an unprecedented market opportunity to embed machine vision in an unlimited number of nontraditional applications that would benefit from automated imaging functionality. As this report indicates, for sensor companies that sell into both the machine vision industry and nontraditional markets, the opportunities outside machine vision far exceed traditional industrial applications.
Backlight Illumination and High Dynamic Range
The ability to back-thin and therefore increase the photoactive area of the sensor to make it more sensitive, is a driving force behind the continuance of CCD sensors. At the same time, high-dynamic-range (HDR) capabilities on CMOS sensors are allowing a sensor to capture a car's license plate in near dark without being blinded by headlights just outside the camera's field of view.
Both approaches to improving the sensitivity of sensors are expected to continue, eventually leading to backlit CMOS sensors that offer more of the benefits of CCD sensors without sacrificing speed and cost.
What the Future Looks Like
As this article shows, machine vision camera technology is accelerating in adoption and diversity, making it possible for amateurs to solve simple vision sensor applications while integrators break new ground in vision-guided robotics, autonomous navigation, and other complex applications. These trends are expected to continue into the foreseeable future as the world finds new ways to empower machines — and people — by helping them see through machine vision lenses.
This article was written by Alex Shikany, Vice President of Membership & Business Intelligence with the Association for Advancing Automation (A3) and Winn Hardin, Contributing Editor for A3. For more information, contact Mr. Shikany at
Kila mwaka unaopita, maono ya mashine yanapata njia yake katika maombi ambayo hayakuwezekana kibiashara - na katika hali zingine, haiwezekani - kabla ya maendeleo mapya ya programu kama vile kujifunza kina na mbinu zinazohusiana za usindikaji wa mtandao wa neva. Ingawa ni miaka 50 kwenye mteremko wa kukomaa, maono ya mashine yanaanza tu kuona uwezo wake kamili wakati inakwenda kuelekea mapinduzi ya viwanda ya pili, iliyoonyeshwa na kuongezeka kwa ushirikiano wa mashine-kwa-mashine na mashine-kwa-binadamu. Ongezeko la matumizi ya maono ya mashine, iliyoongozwa kwa sehemu kubwa na kuunganishwa kwa uchambuzi wa picha za juu, roboti, kujifunza mashine, na automatisering inayohusiana, inaongoza kwa suluhisho za ubunifu, ambazo hazijawahi kuonekana, kwa changamoto za kipekee ndani na nje ya kiwanda. Kama "macho" ya mfumo, kamera za maono ya mashine zitabaki muhimu kwa maendeleo ya suluhisho la ngazi inayofuata katika aina mbalimbali za masoko ya kimataifa yanayokua. Kwa sababu shutters za ulimwengu zilikuja kwa sensorer za macho za semiconductor za oksidi ya chuma (CMOS), soko limedokeza kwa muda mrefu kifo cha detector ya malipo ya pamoja (CCD) kwa sababu ya faida za CMOS. Ina mchakato wa utengenezaji wa bei nafuu, utendaji wa kasi zaidi, na uwezo wa kuunganisha microprocessors, kumbukumbu, na vitalu vingine vya elektroniki kwenye chip (kwa mfano, Xilinx's Xing, Intel's Movidius, Max10, na Cyclone). Lakini kama utabiri mwingi, kila mwaka mpya huleta ripoti mpya na za makosa za kifo cha CCD. CCD ina nafasi kubwa katika sayansi ya maisha na matumizi yanayohusiana kwa sababu ya uwezo wake wa kupunguza kina cha tabaka la silicon nyuma ya sensor (nyembamba nyuma) kwa unyeti mkubwa wa macho, ukosefu wa kelele ya muundo wa kudumu, na sakafu ya chini ya kelele. Hata hivyo, CMOS imechukua zaidi ya soko la maono ya mashine. CCD sensorer bado kutoa chini ya pixel wafu ikilinganishwa na kiwango cha kawaida CMOS chip mavuno na juu quantum ufanisi na responsiveness, ambayo ni muhimu kwa karibu infrared (NIR), sayansi, na maisha sayansi maombi. Hizi ni pamoja na matumizi ya microscopy na endoscopy, ambapo unyeti ni muhimu kwa kupata picha muhimu. Aina nyingine za sensorer ni pamoja na microbolometer na InGaAs sensorer katika IR-thermal maonyesho maombi - mbinu ya kawaida kutumika kufuatilia mali kulingana na joto la uendeshaji katika forges, chuma kazi, na maombi yanayohusiana. Kila baada ya miaka michache, matokeo ya sensorer ya macho hupata kuboresha kutosha kupunguza gharama ambapo wateja wa maono ya mashine wanaweza kuruka kutoka kwa eneo moja la azimio hadi jingine. Hii inashikilia kweli kama sisi ni kuzungumza eneo safu au safu linear, yaani, line scan kamera. Kwa ujumla, azimio zaidi hutoa moja ya faida mbili: ama pikseli zaidi kusaidia kutambua kasoro katika picha (kwa mfano, kasoro ndogo juu ya kupungua kwa alama za wafer), au uwezo wa kufanya ukaguzi zaidi katika uwanja fulani wa mtazamo (kwa mfano, ukaguzi mkubwa wa wafer-flat-paneli) - haswa kama hata kamera za sehemu za "bubu" zinaanza kuongeza maeneo mengi ya kupendeza kwa huduma zao zinazopatikana. Katika 2018, kamera ya kawaida ya sehemu iliondoka kutoka 2 hadi 5 megapixels (MP). Katika miaka ijayo, OEMs ya kamera zinatabiri kuruka hadi 10 MP kama ukubwa wa kawaida wa safu ya sensorer ya maono ya mashine. Hii inaweza kupatikana wakati bado kudumisha pixel ukubwa wa kweli wa microns kadhaa mraba, hivyo kwamba optics kawaida inapatikana bado na uwezo wa kutatua mwanga juu ya pixels moja. Hii ni tofauti na safu kubwa za sensorer katika simu mahiri na optics ya bei nafuu ambayo haiwezi kutoa ahadi ya azimio la kweli la sensorer za 12+ MP na pikseli za 1.4 μm. Kwa upande mwingine, baadhi ya kamera kubwa za maono ya mashine leo kwa sekta ya semiconductor hutoa 50 MP na pikseli za 5μm, kutoa azimio kubwa kwa ukubwa wa pixel inayofaa kwa optics ya kawaida ya kamera. Katika sehemu ya line scan, urefu wa 8k array ni kiwango kipya, juu kutoka 4k line scan. Kwa sababu ya uwezo wa kujenga safu moja tu ya pikseli, kamera za skanning za mstari zinaweza kudumisha ukubwa mkubwa wa pikseli kwa unyeti wa mwanga kwa urahisi zaidi wakati bado zinaendesha kwa kasi ya juu ya kusoma. Katika baadhi ya kesi, wao kutumia <unk>taps<unk> nyingi kusoma nje sehemu ya muda mrefu linear array sensor na wakati uwanja ushirikiano (TDI) bin safu nyingi za pixels katika pixel moja thamani. Mbinu hizo huruhusu kamera mpya ziweze kukabiliana na kuongezeka kwa kasi ya mstari wa utengenezaji. "Watu wa ndani wanasema ""plateau ya mstari wa mstari wa 15k hadi 16k"" ambayo itaweka mahitaji ya ziada kwenye optics ili kukamata mwanga mwingi iwezekanavyo katika urefu kamili wa sensor." Changamoto ya kulinganisha optics na ukubwa mkubwa wa sensor michezo pixels ndogo itaendelea katika siku zijazo kati ya wote eneo na linear safu sensor makundi. Wataalamu wa kamera wanasema pikseli ya 3mm ni mstari ambapo kuendeleza ufanisi, gharama nafuu, optics inakuwa tatizo, kama si vigumu, kutumia teknolojia ya utengenezaji wa macho ya leo. Kasi: Mipangilio ya eneo, Scan ya Mstari: Kasi ya juu, kwa kawaida hufafanuliwa na kiwango cha sura ya kamera, inaruhusu wateja kuchunguza bidhaa zaidi kwa dakika na kusaidia kuzima mwendo wa vitu vinavyohamia haraka wakati wa kupunguza blur. Hii inaboresha uwezo wa mfumo wa maono ya mashine kufanya uamuzi sahihi wa kiotomatiki au kipimo. Sasa, kasi ya fps ya 200 kwa safu za eneo na viwango vya fremu za 70kHz kwa safu za 4k za mstari ni za kawaida, na viwango vya fremu za juu mara mbili hadi tatu juu ya viwango hivyo. Kwa bahati nzuri, kamera za macho za mashine zina uwezo wa kufanya kazi kwa kiwango cha 100% cha fremu kamili. Badala yake, maombi ya kawaida kupata picha kadhaa katika kipindi kifupi sana cha muda na kisha kutuma data kwa kitengo cha usindikaji katika mlipuko, kupunguza mahitaji ya bandwidth juu ya mitandao ya picha. GigE Vision inaweza kukaribisha idadi kubwa ya maombi na viwango vya fremu na mahitaji ya upana wa bendi ya uhamisho wa data ya kamera zinazohusiana. Wataalamu wa IOT katika maombi ya mtandao wa picha wanasema wanaona maslahi katika 2.5Gbps GigE Vision juu ya NBA SE-T kama hatua ya kati kuelekea 10Gbps. Kwa sasa, soko la watumiaji bado halijakubali 10GigE kama bandari ya default katika desktops na laptops kwa sababu ya gharama, matumizi ya nguvu, na changamoto za muundo wa joto. Kwa ujumla, OEMs ya kamera za maono ya mashine ya eneo na safu za mstari wanaamini kwamba wakati kesi maalum zitatokea wakati wowote ambapo wateja wanataka kamera zinazoendesha kwa kasi ya juu, maombi mengi yanatumika kwa kutosha na matoleo ya kamera ya leo. Kama bottleneck ipo kwa ajili ya maombi fulani, ni kawaida katika mtandao katika hatua hii. Hii inahitaji hoja kuelekea frame grabber <unk> msingi itifaki kama vile Kamera Link High Speed, ambayo ina throughput ya juu kuliko kiwango GigE Vision na USB3 Vision. "Kama ilivyoelezwa hapo juu, ""Integrators ya Maono ya Mashine inakadiria kwamba licha ya ukuaji wa kawaida wa kamera za maono ya rangi, 90% ya maombi ya maono ya mashine yanatibiwa na kamera ya monochrome.""" Kwa kawaida, sababu imekuwa gharama, kama rangi kamera ni ghali zaidi kuliko monochrome kamera na azimio sawa. "Kulingana na ""gharama kwa pixel"" ya kamera, kamera ya rangi hutumia kichujio cha Bayer, ambayo hupunguza azimio la kamera hadi theluthi moja ya azimio la awali." Kwa ajili ya uwakilishi sahihi zaidi wa rangi, kamera za 3CCD zimekuwa kiwango. Kamera hizi hutumia prism kutenganisha rangi, badala ya filters kutumika moja kwa moja kwa safu moja. Katika kesi hii, kila sensor hupata moja rangi channel, na picha tatu tofauti ni pamoja katika programu ya kuzalisha uwakilishi zaidi ya kweli ya kitu kuliko ni kupatikana kwa kutumia filters Bayer. Kwa kuongezea, mbinu hii ya gharama kubwa haija na adhabu ya azimio. Kwa ajili ya maombi sahihi zaidi ya ukaguzi wa uchapishaji, kwa mfano, prism-msingi chip line scan kamera wamekuwa kiwango cha dhahabu kwa miaka. Hii imekuwa kutokana na usahihi wa uwakilishi wa rangi, uwezo wa kupunguza spectral overlap kati ya njia, na kuboreshwa nafasi alignment kati ya njia za rangi. Katika mwaka uliopita, soko la kamera ya maono ya mashine liliona kamera ya kwanza ya skanning ya mstari kutoa vichungi tofauti vya polarization vinavyotumika kwa safu tofauti, kipengele muhimu kwa ukaguzi wa kuonyesha ya jopo tambarare. OEMs kutarajia mwenendo huu kuelekea multichannel, customized kuchuja kuendelea. Taa pia inachukua jukumu lenye nguvu zaidi katika suluhisho la maono ya mashine kwa ujumla, lakini katika matumizi ya rangi haswa. Kamera na exposures nyingi na mikoa ya maslahi inaweza kufanya ukaguzi nyingi, wengi ambao inaweza kuhitaji mbinu tofauti ya taa. Baadhi ya OEMs kamera ni kutumia single-sensor kamera na muda domain badala ya upimaji optical kutenganisha njia za rangi. Hii husababisha ufumbuzi wa multispectral na throughput ya chini na gharama ya chini lakini inahitaji vyanzo vya taa na udhibiti tata zaidi. Maombi ya kawaida kwa kamera za maono ya mashine bado hutumia mwanga unaoonekana. Hata hivyo, matumizi fulani hutumia njia za taa zisizo maarufu sana, kama vile X-ray, ultraviolet (UV), karibu na infrared (NIR), infrared ya mawimbi mafupi (SWIR), na infrared ya mawimbi marefu (LWIR). Moja ya matumizi ya kawaida kwa ajili ya X-ray taa ni ukaguzi metallurgy, ambayo inaonyesha kasoro za muundo au uchovu katika vipengele chuma. NIR ni njia ya kuonyesha picha za hali ya juu katika matumizi kama vile ukaguzi wa chakula, ambapo inawezekana kutambua bidhaa zisizo za kikaboni au zilizokufa ambazo zinaweza kuwapo kati ya zile safi. Maombi mengine ya ukaguzi wa kilimo hutumia taa ya NIR kutambua matunda au mboga zilizooza. NIR pia hutumiwa katika usimamizi wa taka, kuchakata, na katika sekta ya semiconductor. UV, SWIR, na LWIR bado ni niche sana kwa ajili ya maombi ya maono ya mashine. Kwa kuwa maono ya roboti yanazidi kuwa ya kawaida na maono ya mashine yanaongeza msaada wake kwa matumizi zaidi ya sakafu ya kiwanda, hitaji la maono sahihi ya mashine ya 3D huongezeka. "Kama ilivyoelezwa hapo juu, ""Stereovision"" ni aina ya kamera ya 3D ambayo hutumia kamera nyingi kugundua nafasi ya 3D kulingana na mabadiliko madogo ya mtazamo, 3D ya kamera moja, ambapo picha zinapatikana kutoka maeneo tofauti na hutumia usindikaji wa mtazamo sawa na 3D ya 3D, kamera moja, ama eneo au scan ya mstari, na makadirio ya mwanga iliyoundwa, na kamera ya static na vyanzo vingi vya taa zisizo na muundo zilizowekwa karibu na lengo." Kuna chaguzi nyingi na zitaendelea kuongezeka. Kwa sababu hiyo, watengenezaji wengi wa kamera za maono ya mashine sasa hutoa kamera za 3D zilizoboreshwa na mifumo ya kipimo iliyopangwa kiwanda ambayo inawezesha ushirikiano wa maono ya 3D. Zaidi ya hayo, GeniCam 3.0 imeboresha utambuzi na programu ya ufumbuzi wa kawaida wa kamera ya 3D, kuondoa moja ya changamoto za mwisho zilizobaki kwa kuongezeka kwa kupitishwa kwa soko la kamera ya 3D. "Kama ilivyoelezwa hapo juu, ""Mazungumzo yoyote ya maono yaliyoingizwa yanahitaji kuanza na majadiliano juu ya siku zijazo za kamera za akili, kwa sababu kamera za akili zinawakilisha matumizi ya kwanza ya wingi kwa teknolojia ya maono yaliyoingizwa.""" Mifumo ya maono ya mashine iliyoingizwa huchanganya vipengele vyote vya mfumo wa maono ya mashine, pamoja na upatikanaji wa picha, usindikaji wa picha, na mawasiliano katika sababu ya fomu ambayo inashinda ukubwa mdogo na matumizi ya nguvu ya chini. Hapo awali, ushirikiano wa kamera ya smart ulifanywa katika ngazi ya bodi. Mifumo hii kuweka vipengele vyote juu ya bodi chache iwezekanavyo katika enclosure moja. Mwaka 2018 ilikuwa mwaka wa kwanza kwa makampuni ya kuona mashine kupata ufumbuzi mpya wa mfumo kwenye chip (SoC) kama vile Xilinx Xing, ambayo inachanganya cores za ARM na safu za FPGA kwenye chip moja. Sekta bado ni kusubiri kwa kiasi cha upatikanaji wa Intel Movidius SoC chipset. Ni salama kusema kwamba hii mpya sana jumuishi maono SoC ufumbuzi inaweza kwa kiasi kikubwa kubadilisha uwezo smart kamera, utendaji na gharama. "Hii itafungua fursa ya soko isiyo na kifani ya kuingiza maono ya mashine katika idadi isiyo na kikomo ya maombi yasiyo ya jadi ambayo yatanufaika na utendaji wa picha ya kiotomatiki. """ Kama ripoti hii inaonyesha, kwa makampuni ya sensor ambayo kuuza katika sekta ya mashine ya maono na masoko yasiyo ya jadi, fursa nje ya mashine ya maono kupita mbali matumizi ya jadi ya viwanda. Backlight, mwangaza wa nyuma na High Dynamic Range: Uwezo wa back-thin na hivyo kuongeza eneo photoactive ya sensor kufanya hivyo nyeti zaidi ni nguvu ya kuendesha nyuma ya kuendelea kwa sensorer CCD. "Kama vile, uwezo wa ""high dynamic range"" (HDR) kwenye sensorer za CMOS inaruhusu sensorer kukamata nambari ya gari karibu na giza bila kufichwa na taa za mbele nje ya uwanja wa kuona wa kamera." Njia zote mbili za kuboresha unyeti wa sensorer zinatarajiwa kuendelea, na hatimaye kusababisha sensorer za CMOS zilizo na backlit ambazo hutoa faida zaidi za sensorer za CCD bila kutoa sadaka ya kasi na gharama. Kama makala hii inaonyesha, teknolojia ya kamera ya maono ya mashine inaongeza kasi katika kupitishwa na utofauti, ikifanya iwezekane kwa wapenzi kutatua maombi rahisi ya sensorer ya maono wakati wajumuishaji wanavunja ardhi mpya katika roboti inayoongozwa na maono, urambazaji wa kujitegemea, na matumizi mengine magumu. Mwelekeo huu unatarajiwa kuendelea katika siku zijazo zinazotazamiwa wakati ulimwengu unapopata njia mpya za kuwezesha mashine <unk> na watu <unk> kwa kuwasaidia kuona kupitia lensi za maono ya mashine. Alex Shikany, Mkurugenzi Msaidizi wa Ushirikiano na Ujasusi wa Biashara katika Chama cha Kuendeleza Automation (A3A) na Winn Hardin, Mhariri Msaidizi wa A3A Kwa maelezo zaidi, wasiliana na Shikari katika: