“The industry’s critical focus has shifted from pursuing extreme performance in individual technologies to deep hardware-software integration and systemic collaboration,” says Robin Xia, R&D Technical Director, Dahua Technology.
What are the most important emerging technologies shaping the future of security today?
Achieving high-quality full-colour imaging in low-light environments while enhancing the capture of moving objects is a significant challenge in the current field of visual technology.
This requires deeper integration of algorithms and hardware, as well as systematic co-design to optimise overall performance.
On one hand, advanced image sensors, optical components and AI-ISP algorithms must be tightly integrated to restore true colours and suppress motion blur under low-light conditions.
On the other hand, the system’s intelligent capabilities must evolve from rigid functional modes to supporting flexible customisation by users based on real-world scenarios.
This means users can independently define detection rules and response logic for various monitoring scenarios, target types and environmental conditions – a shift from general intelligence to scenario intelligence.
This drives imaging systems toward greater precision and adaptability to complex demands.
How does the shift toward system-level integration in low-light imaging reflect broader changes in security technology strategies?
As the technical parameters of optical lenses and image sensors continue to advance, they are gradually approaching physical limits in key metrics such as aperture and pixel size.
While relying solely on optical design or hardware specification iterations can yield marginal improvements, it has become difficult to achieve transformative breakthroughs in image quality or user experience.
At the same time, the computational power and memory capacity of security system main control chips also constrain the complexity of AI-ISP models.
In this context, even advanced AI-ISP (Artificial Intelligence Image Signal Processing) algorithms – capable of optimising image quality, suppressing noise and enhancing dynamic range – are ultimately bound by underlying hardware limitations, further highlighting the constraints of single-technology approaches.
Therefore, the industry’s critical focus has shifted from pursuing extreme performance in individual technologies to deep hardware-software integration and systemic collaboration.
Optical components, sensors, chip computing power, storage systems and intelligent algorithms must be treated as an organic whole.
Through architectural optimisation, resource scheduling and algorithm lightweighting, greater efficiency can be unlocked within existing hardware constraints.
More importantly, all efforts must be closely aligned with real-world customer scenarios and business needs – avoiding a pure technology race and instead focusing on delivering tangible value to users.
Based on this perspective, the future of security hinges on two major shifts:
First – from “passive recording” to “active sensing and decision-making.” Traditional security relies on post-event verification through manual review and analysis, whereas modern intelligent systems should integrate video structuring, behavior analysis, multimodal sensing and real-time risk assessment to enable instant identification, alerts and preliminary response to anomalies and safety hazards – transforming “seeing clearly” into “understanding, warning and aiding decisions.”
Second – from a “product-centric” to a “solution-centric” approach. This means moving beyond isolated sales of cameras, storage or analytics software and instead providing end-to-end system solutions tailored to different scenarios (e.g., smart transportation, community governance, industrial safety, commercial retail). This includes scenario-optimised hardware selection, configurable algorithm models, open platform interfaces and sustainable maintenance services, ultimately helping customers solve real-world management challenges, improve operational efficiency and drive business value.
In summary, only through systemic innovation – driven by hardware-software synergy, algorithm empowerment and demand-oriented design – can the industry carve out new development paths during this technological plateau.
This will propel security from a “perception tool” to an “intelligent engine,” truly integrating into the digital transformation of industries across the board.
How does improved full-colour visibility at night impact risk assessment and incident response in critical environments?
Night-time full-colour technology, leveraging large-format sensors and enhanced ISP algorithms, breaks through the limitations of low-light colour reproduction.
In critical scenarios such as substations and farms, its ability to accurately identify targets and behavioural characteristics significantly reduces false alarms at night, enhancing the foresight and reliability of risk assessment.
The system achieves 24/7 detection, supporting intelligent detection and precise alerts based on target colour features, ensuring traceability and analysis of night-time incidents to build a closed-loop, all-weather active defence.
This not only enriches the feature database but also upgrades algorithms from single-dimensional contour judgment to multi-dimensional comprehensive analysis.
This dramatically shortens response cycles, transforming risk management from passive reaction to proactive intervention, effectively strengthening situational control and decision-making efficiency in critical environments.
How critical is motion-aware AI in addressing real-world challenges like false positives and missed detections in complex environments?
In complex environments, especially low-light conditions, false alarms and missed detections severely hinder security effectiveness. Motion-sensing AI, by precisely analysing dynamic targets, emerges as a key solution to this challenge.
Traditional detection methods struggle to distinguish animals or environmental changes, frequently triggering invalid alerts and increasing operational verification burdens.
More critically, motion blur introduces dual risks: on one hand, it causes loss of target features (e.g., blurred contours, aliased textures), turning fast-moving humans or vehicles into “ghost-like” artifacts that algorithms fail to recognise, significantly raising missed detection rates.
On the other hand, blur intertwined with background noise creates false motion zones, easily misjudged as intrusions or abandoned objects, sharply increasing false alarm rates.
Motion-sensing AI, through large-format sensors and dynamic compensation algorithms, effectively suppresses blur interference, ensuring accurate extraction of multi-dimensional features (e.g., shape, colour) in 24/7 operations.
This not only boosts detection accuracy to 99% but also intelligently filters out animal and environmental false alarms, shortening incident response cycles.
Field tests confirm that this technology enhances operational efficiency, converting passive alerts into proactive risk prediction and solidifying situational control and decision-making reliability in complex scenarios.
As imaging approaches near-daylight clarity at night, how does this redefine expectations for situational awareness and operational decision-making?
In the past, constrained by technological limitations, night-time imaging commonly suffered from noise interference and motion blurring issues, resulting in significantly inferior image quality compared to daytime performance.
This not only restricted the real-time detection accuracy of intelligent algorithms but also compromised the reliability of post-event target verification.
Today, with the maturation of technologies such as AI-ISP and ultra-large aperture lenses, nighttime imaging can now deliver clarity comparable to daytime conditions.
This enables intelligent algorithms to achieve truly all-weather, round-the-clock precision perception: nighttime detection accuracy matches daytime levels and proactive surveillance effectiveness is substantially enhanced.
This breakthrough ensures faster and more precise target localisation, shortening incident response cycles.
Consequently, situational awareness has evolved from “passive reaction” to “active prediction,” providing more reliable and timely multi-dimensional data support for operational decision-making. This transformation has redefined performance standards in the security industry.
