Hyperspectral imaging, while still a smaller share of installed systems due to higher camera cost and slower frame rates, is gaining ground in food sorting, pharmaceutical tablet inspection, and recycling sortation, where material composition - not just shape - determines quality. A hyperspectral system can distinguish between visually identical plastics by polymer type based on their reflectance signature across dozens of narrow wavelength bands, something no RGB camera can achieve. Software platforms are responding by adding native support for hypercube data processing rather than treating these sensors as an afterthought requiring custom scripting.
Why Standard Machine Vision Sensors Struggle After Dark Conventional CMOS sensors used in many industrial cameras are built with color filter arrays tuned for human-perceived color accuracy under daylight or controlled machine-vision lighting. When ambient light drops into the near-infrared band - roughly 700 to 1000 nanometers - these sensors often include an infrared-cut filter specifically to block that wavelength range, because uncorrected NIR light distorts color rendering during daytime inspection tasks. The unintended consequence is that the same filter which improves daytime color fidelity actively suppresses the signal a security application needs most during low-light hours.
The commercial pressure to reduce integration time compounds the problem. When engineers are racing to commission a line, it is common to leave manufacturer default passwords in place on smart cameras, expose web-based configuration tools without TLS, or grant broad administrative rights to service technicians who only need read-only diagnostic access. None of these shortcuts are malicious, but each one widens the attack surface unnecessarily, and each is avoidable with modest planning during the specification phase rather than after deployment.
Expect a premium of roughly 30-60% over a comparable visible-light-only industrial camera, driven mainly by the specialized sensor coating and, where applicable, mechanical day/night filter assemblies. Lens costs can add further if apochromatic correction across visible and NIR bands is required.
Why Machine Vision Systems Are an Attractive Attack Surface Machine vision hardware sits at an unusual intersection of information technology and operational technology. Cameras stream continuous data over Ethernet, software platforms expose configuration interfaces for remote diagnostics, and many installations still rely on legacy protocols such as unencrypted GigE Vision or FTP-based image transfer because those protocols were designed for throughput, not confidentiality. An attacker who gains access to a poorly segmented vision network can potentially manipulate calibration files, inject false pass results into quality control software, or pivot toward safety-critical robotic guidance systems.
This trade-off becomes clearer with a simple worked comparison. Suppose two cameras are evaluated for monitoring a 30-meter warehouse aisle using 850nm illuminators rated at 20 watts. Camera A, at 12 megapixels with small pixels, produces a technically sharper daytime frame but shows visible noise and motion blur beyond 15 meters at night. Camera B, at 3 megapixels with larger NIR-enhanced pixels, resolves a forklift's license plate and driver silhouette reliably out to 28 meters under the same illuminator. For the security use case, Camera B is the better engineering choice even though its spec sheet looks less impressive on paper.
Working distance and field of view calculations also depend on pixel pitch through the concept of spatial resolution per pixel, sometimes called the ground sample distance in vision engineering. If an application requires resolving a 0.1 mm feature reliably, the rule of thumb calls for at least two, and ideally three, pixels across that feature to avoid aliasing artifacts. Suppose a system needs to inspect a 50 mm wide part with 0.1 mm defect sensitivity: dividing the field of view by the required pixel count per feature indicates that a sensor needs at least 1,500 effective pixels across that width, which then informs both the sensor's native resolution and the lens focal length needed to fill the field of view at the chosen working distance. Machine vision cameras
CoaXPress, by contrast, was engineered from the outset for exactly this scenario. A single CXP-12 link now delivers 12.5 Gbps of sustained throughput, and multi-connector configurations scale linearly, so four coaxial cables can move 50 Gbps to a frame grabber without resorting to custom protocols or proprietary compression. This matters most in applications like flat-panel display inspection, printed circuit board verification, and battery electrode scanning, where full-resolution, uncompressed frames are non-negotiable for defect classification algorithms to work reliably.
Yes, unless the camera includes a mechanical or electronic filter switch. Removing the IR-cut filter permanently improves low-light NIR performance but introduces color shift and washed-out contrast in daytime color inspection, so dual-purpose deployments generally need a switchable filter mechanism.
Why Standard Machine Vision Sensors Struggle After Dark Conventional CMOS sensors used in many industrial cameras are built with color filter arrays tuned for human-perceived color accuracy under daylight or controlled machine-vision lighting. When ambient light drops into the near-infrared band - roughly 700 to 1000 nanometers - these sensors often include an infrared-cut filter specifically to block that wavelength range, because uncorrected NIR light distorts color rendering during daytime inspection tasks. The unintended consequence is that the same filter which improves daytime color fidelity actively suppresses the signal a security application needs most during low-light hours.
The commercial pressure to reduce integration time compounds the problem. When engineers are racing to commission a line, it is common to leave manufacturer default passwords in place on smart cameras, expose web-based configuration tools without TLS, or grant broad administrative rights to service technicians who only need read-only diagnostic access. None of these shortcuts are malicious, but each one widens the attack surface unnecessarily, and each is avoidable with modest planning during the specification phase rather than after deployment.
Expect a premium of roughly 30-60% over a comparable visible-light-only industrial camera, driven mainly by the specialized sensor coating and, where applicable, mechanical day/night filter assemblies. Lens costs can add further if apochromatic correction across visible and NIR bands is required.
Why Machine Vision Systems Are an Attractive Attack Surface Machine vision hardware sits at an unusual intersection of information technology and operational technology. Cameras stream continuous data over Ethernet, software platforms expose configuration interfaces for remote diagnostics, and many installations still rely on legacy protocols such as unencrypted GigE Vision or FTP-based image transfer because those protocols were designed for throughput, not confidentiality. An attacker who gains access to a poorly segmented vision network can potentially manipulate calibration files, inject false pass results into quality control software, or pivot toward safety-critical robotic guidance systems.
This trade-off becomes clearer with a simple worked comparison. Suppose two cameras are evaluated for monitoring a 30-meter warehouse aisle using 850nm illuminators rated at 20 watts. Camera A, at 12 megapixels with small pixels, produces a technically sharper daytime frame but shows visible noise and motion blur beyond 15 meters at night. Camera B, at 3 megapixels with larger NIR-enhanced pixels, resolves a forklift's license plate and driver silhouette reliably out to 28 meters under the same illuminator. For the security use case, Camera B is the better engineering choice even though its spec sheet looks less impressive on paper.
Working distance and field of view calculations also depend on pixel pitch through the concept of spatial resolution per pixel, sometimes called the ground sample distance in vision engineering. If an application requires resolving a 0.1 mm feature reliably, the rule of thumb calls for at least two, and ideally three, pixels across that feature to avoid aliasing artifacts. Suppose a system needs to inspect a 50 mm wide part with 0.1 mm defect sensitivity: dividing the field of view by the required pixel count per feature indicates that a sensor needs at least 1,500 effective pixels across that width, which then informs both the sensor's native resolution and the lens focal length needed to fill the field of view at the chosen working distance. Machine vision cameras
CoaXPress, by contrast, was engineered from the outset for exactly this scenario. A single CXP-12 link now delivers 12.5 Gbps of sustained throughput, and multi-connector configurations scale linearly, so four coaxial cables can move 50 Gbps to a frame grabber without resorting to custom protocols or proprietary compression. This matters most in applications like flat-panel display inspection, printed circuit board verification, and battery electrode scanning, where full-resolution, uncompressed frames are non-negotiable for defect classification algorithms to work reliably.
Yes, unless the camera includes a mechanical or electronic filter switch. Removing the IR-cut filter permanently improves low-light NIR performance but introduces color shift and washed-out contrast in daytime color inspection, so dual-purpose deployments generally need a switchable filter mechanism.