Thermal imaging company, HIKMICRO, has launched two algorithmic functions: SuperScene and SuperScene+.
Both products follow on from SuperDetect, HIKMICRO’s first proprietary algorithm that identifies problematic water leaks by analysing sound frequency, intensity, and spatial characteristics.
“We are renowned for delivering high quality thermal imaging technology to the mass market in an accessible and cost-effective manner, but this is a transformational chapter, even by our standards,” claims Stefan Li, HIKMICRO Overseas General Manager. “These ultra smart algorithms optimise detection like never before and bridge the gap between complexity and simplicity, turning advanced diagnostics into actionable insights for first time users and seasoned professionals alike.”
Designed to identify air leaks, heat loss, and moisture damage in homes, SuperScene is available on HIKMICRO’s handheld and pocket devices (Eco, Eco-V, B20S, B21LS, PocketE, Pocket2, running V 5.5.82) and is a deep learning algorithm intended to simplify the inspection process and make home inspections faster. It focuses on a vast collection of thermal images representing typical issues such as insufficient insulation and water leakage. This seeks to enable the software to quickly and accurately identify potential insulation deficiencies and suspicious water leaks during Infrared (IR) inspections. The product is currently available in beta.
For experienced, professional users, HIKMICRO’s 256×192 IR resolution handheld and pocket series cameras (B20S, B21LS, Pocket2, running V 5.5.82) also offer a new Pro Scene function, whereby a colour algorithm works with additional tools like hygrometers to analyse and alarm thermal images based on temperature and humidity data. During IR home inspections, this should help pinpoint potential insulation defects (marked with a blue alarm) and suspicious moisture issues (marked with a green alarm). A scene pre-set quick switch also enables customisation for different scenarios.
SuperScene+ brings automated risk detection for electrical panel inspection and PCB safety, aiming to simplify workflows via built-in algorithms that identify temperature measurement targets in specific scenarios and determine if anomalies exist.
In electrical panel inspection, image recognition and Delta T (ΔT) algorithms are combined to locate overloaded/degraded fuses through absolute temperature alarms and relative difference warnings. Micro-temperature sensing can also, according to the company, detect thermal differences as low as 0.5°C, flagging potential hazards before they escalate.
For PCB inspection, pre-configured templates plus AI calibration can batch screen high-density boards to identify short circuits, cold joints, and overloads.
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