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	Part #	Description	CATEGORY	SUB CATEGORY	Camera Type	Resolution [MPixel]	Width [Pixel]	Height [Pixel]	FrameRate [f/s]	Sensor Format	Pixel W[µm]	Pixel H[µm]	Interface	Sensor Type	Shutter Type	Bit_Depth	MANUFACTURER	Size	Datasheet	Sensor Manufacturer	Sensor Model	Full well capacity	Type	Auto Iris	Mount	Voltage	Temp. Range	Weight[g]	Power	Illumination Color	HDMI Output	Line Rate [Kline/s]	Pixel Rate (MHz)	Host Interface	OS Support	Programmable FPGA	F#	MOD	Focus Type	Iris Type	Zoom Type	Opt. Mag.	DRAWING	NA	IF Side1	IF Side2	Freq range [MHz]	PoCL	Number of Channels	Environmental	Max Current Continuous	Max Current Pulsed	INTERFACING	Minimum Pulse Width (μS)	SafeSense	SafePower	Max. Memory	Processor Speed	Control	USERMANUAL	Co-axlight	MaxBitrate	ColorMono	Discontinued Product	Sensor Format Range	Frame_Rate_Range	Resolution_Range	Focal_Length_range	Focal_Length
	Part #	Description	CATEGORY	SUB CATEGORY	Camera Type	Resolution [MPixel]	Width [Pixel]	Height [Pixel]	FrameRate [f/s]	Sensor Format	Pixel W[µm]	Pixel H[µm]	Interface	Sensor Type	Shutter Type	Bit_Depth	MANUFACTURER	Size	Datasheet	Sensor Manufacturer	Sensor Model	Full well capacity	Type	Auto Iris	Mount	Voltage	Temp. Range	Weight[g]	Power	Illumination Color	HDMI Output	Line Rate [Kline/s]	Pixel Rate (MHz)	Host Interface	OS Support	Programmable FPGA	F#	MOD	Focus Type	Iris Type	Zoom Type	Opt. Mag.	DRAWING	NA	IF Side1	IF Side2	Freq range [MHz]	PoCL	Number of Channels	Environmental	Max Current Continuous	Max Current Pulsed	INTERFACING	Minimum Pulse Width (μS)	SafeSense	SafePower	Max. Memory	Processor Speed	Control	USERMANUAL	Co-axlight	MaxBitrate	ColorMono	Discontinued Product	Sensor Format Range	Frame_Rate_Range	Resolution_Range	Focal_Length_range	Focal_Length

Features

PEKAT VISION contains the right set of self-learning tools. These tools can be combined and interwoven with a scripting code. Our experience has shown that exactly these tools together can tackle practically any vision task in manufacturing.

Learn how to connect to a camera, integrate e.g. with Labview, calculate statistics and much more on our github.

Unsupervised anomaly detector

Unsupervised anomaly detector can be trained by positive (error-free) examples. It is enough to provide just a few example images. It is able to inspect stable objects or even completely unstable surfaces like deformed textile with pattern.

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Supervised surface check

Supervised surface check is a tool which can be trained to find defects on a completely heterogeneous surfaces. Example of such defects: rust, abrasion, leakage etc.

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Object detector and classifier

Object detector and classifier can find objects with unstable shape - e.g. knots on wood. It doesn't even matter if the objects are rotated.

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Inspection modules

Inspection modules can be combined to a complex flow and even interwoven with custom image preprocessing or scripting code.

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OCR

OCR (Optical character recognition) module is used for finding individual characters or words in the image.

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Unifier

Unifier can be used when objects which are to be inspected are rotated or the position is scattered in the images. Unifier unifies the position and rotation of the objects in the images for further processing.

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Preprocess module

Preprocess module module is a tool for easy image editing before the next processing. It allows rotation, cropping, scaling, background normalization, background removal and many more transformations.

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Auto sensitivity

Auto sensitivity is a part of the anomaly detection module and determines the best sensitivity value and can ideally find the border between good product and defective product.

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Measurement

Measurement module serves for simple measurement of dimensions of object.

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Runtime statistics

Runtime statistics shows statistics of OK and NOK images sent from API according to specific date and time you choose.

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Statistics

Statistics module calculates how successful the application is at evaluating images. Shows confusion matrix and related metrics and also min, max and average processing times.

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Report generator

Report generator is a part of Statistics module. It automatically generates HTML report including all the information from statistics, plus training images (if chosen), evaluated testing images and a graph of used modules.

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Python code

Python code gives you high flexibility. You can e.g. preprocess images (e.g. with OpenCV and Numpy), add custom logic or even call external interfaces.

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Output

Output can be used to trigger an action once an image from the camera is processed (All/Good/Bad). You can use the command line (e.g. to run a script), send an HTTP request (GET or POST) or establish connection using Profinet or TCP protocol to your PLC.

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