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ПодробнееQuick ViewASTROHN-2A
Thermal imaging complex for the railway infrastructure security
The ASTROHN-2A thermal imaging modules are the basic elements of the distributed security system for the railway and railway facilities. ASTROHN-2A includes the last achievements at the field of thermal imaging surveillance and video analysis. Thermal imaging module ASTROHN-2A is the block of the full factory readiness installed on the catenary masts and connected to the data acquisition and handling system (DAHS) by the optical fiber. The special mounting at the mast excludes the complex’s periodic motion at the high-speed train passing and does not influence the built-in intellectual video analysis system. The modules are designed considering the ASTROHN-like security systems for the contiguous areas of Gazprom and Russian Railways significant operation experience. ASTROHN-2 is in serial production since 2009. By 2016 there are more than 3,000 complexes in operation. The operating conditions are: 24/7, all year round. The maintenance service includes warranty maintenance and post-guarantee maintenance with replacement if its necessary within 24 hours at the ASTROHN’s service centers.
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ПодробнееQuick ViewASTROHN-IQ384
Thermal imaging camera
Thermal imaging network camera ASTROHN-IQ384 is intended for locating objects (humans, motor vehicles) thermal emissions without external highlighting. This camera has hermetical cover (leakage class IP66) with an germanium aperture. Aperture has an diamond-like durable coating. All germanium windows of ASTROHN-IQ384 thermal imaging systems have built-in defrosting system for the frigid climate. Camera has a pulse overvoltage protection and a protection tripping unit designed for the Russian power lines, and it also has a built-in intellectual video analytics with simple and friendly configuration on basic threat models. Video analytics includes the object recognition and identification function (human, animal, technique, etc). Image and video analysis results output is implemented with Ethernet.
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ПодробнееQuick View
ASTROHN-IQ640
Thermal imaging camera
Thermal imaging network camera ASTROHN-IQ640 is intended for locating objects (humans, motor vehicles) thermal emissions without external highlighting. This camera has hermetical cover (leakage class IP66) with an germanium aperture. Aperture has an diamond-like durable coating. All germanium windows of ASTROHN-IQ640 thermal imaging systems have built-in defrosting system for the frigid climate. Camera has a pulse overvoltage protection and a protection tripping unit designed for the Russian power lines, and it also has a built-in intellectual video analytics with simple and friendly configuration on basic threat models. Video analytics includes the object recognition and identification function (human, animal, technique, etc). Image and video analysis results output is implemented with Ethernet.
This section presents IP thermal imagers with high sensitivity, reliability and climate protection. All thermal imagers complete with high quality domestic germanium IR lenses with high optical efficiency that help to achieve the optimal performance variables of thermal sensitivity and thermal image contrast.
The significant feature of all thermal imagers is the presence of the built-in intellectual data-mining and image processing (video analytics) function. The built-in video analytics in comparison with using the one on server allows to bate demands to the server and therefore its price and to increase the reliability and fail-safety of the system at a whole. The video analytics applied on the thermal image significantly differs from the cameras video analytics due to the features of thermal fields visualisation. Therefore, video analytics systems for the security cameras are not applicable with the thermal imagers. Our company developed special unique algorithms optimized for the work with thermal images that allow to use its benefits with maximum efficiency.
The algorithms used in optic electronic security and surveillance ASTROHN systems allow to build an 3D scene model in the context of horizon and projection, classify and select the observed events by these factors:
