Rail Vision Ltd

Sharon Bason

Shahar Hania

Avraham Ram Guissin

Michael Levy

Yotam Azriel

Zohar Zeev Shechter

Sagi Dolberg

Natalie Day

Avishay Alfandary

A system for verifying a selection of an optical sensor of two or more optical sensors may include: a first optical sensor; a second optical sensor; and a processing unit configured to: receive a selection of one of the first and second optical sensors to provide a selected optical sensor and a non-selected optical sensor; change parameters of the selected optical sensor to a selected set of parameters; receive image frames from the selected optical sensor; change parameters of the non-selected optical sensor to a predefined set of parameters; receive image frames from the non-selected optical sensor; determine that the selected optical sensor operates according to the selected set of parameters and/or that the non-selected optical sensor operates according to the predefined set of parameters; and verify, based on at least one of the determinations, the selection of the optical sensor.

System and method for verifying a selection of an optical sensor

A system for detection and identification of objects and obstacles near, between or on railway comprise several forward-looking imagers adapted to cover each different range forward and preferably to be sensitive each to different wavelength of radiation, including visible light, LWIR, and SWIR. The substantially homogeneous temperature along the rail the image of which is included in an imager frame assists in identifying and distinguishing the rail from the background Image processing is applied to define living creature in the image frame and to distinguish from a man-made object based on temperature of the body. Electro optic sensors (e.g. thermal infrared imaging sensor and visible band imaging sensor) are used to survey and monitor railway scenes in real time.

System and method for object and obstacle detection and classification in collision avoidance of railway applications

Systems and methods for rails and obstacles detection based on forward-looking electrooptical imaging, novel system architecture and novel scene analysis and image processing are disclosed. The processing solution utilizes a deep learning semantic scene segmentation approach based on a rails and switches states detection neural network that determines the railway path of the train in the forward-looking imagery, and an objects and obstacles detection and tracking neural network that analyzes the vicinity of the determined railway path and detects impending obstacles.

Method and system for railway obstacle detection based on rail segmentation

A system for detection and identification of objects and obstacles near, between or on railway comprise several forward-looking imagers adapted to cover each different range forward and preferably to be sensitive each to different wavelength of radiation, including visible light, LWIR, and SWIR. The substantially homogeneous temperature along the rail the image of which is included in an imager frame assists in identifying and distinguishing the rail from the background. Image processing is applied to define living creature in the image frame and to distinguish from a man-made object based on temperature of the body. Electro optic sensors (e.g. thermal infrared imaging sensor and visible band imaging sensor) are used to survey and monitor railway scenes in real time.

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Rosh-Ha′ayin, Israel

Sharon Bason