Edge AI for Roadside and Outdoor Scenarios: Wide-Temperature Rugged Design Solves Outdoor Deployment Challenges
The biggest challenge in outdoor edge AI scenarios—such as intelligent transportation roadside perception, smart campus perimeter security, PV/wind farm monitoring, and mining slope protection—has never been algorithm accuracy. It is whether the hardware can withstand extreme outdoor environments. Under summer sun exposure, the temperature inside cabinets can exceed 70°C, causing ordinary equipment to crash from overheating. In winter, northern low temperatures can drop below -20°C, preventing normal cold starts. Outdoor dust, humidity, and voltage fluctuations all significantly shorten the service life of ordinary AI devices and increase O&M costs.
With comprehensive industrial-grade hardware design, the EA Series industrial edge AI computers serve as reliable compute carriers for outdoor edge AI scenarios. The entire series supports -20°C to +75°C wide-temperature full-load operation and has passed rigorous high/low-temperature aging tests. Even under extreme conditions such as roadside cabinets exposed to summer sun or outdoor extreme cold in Northeast China in winter, they maintain stable operation without throttling, crashes, or startup failures. All aluminum body, with excellent dust and moisture resistance, can be directly installed in outdoor cabinets, pole station chassis, without additional air conditioners, cooling fans and other equipment, greatly reducing deployment costs and operation and maintenance frequency.
In intelligent transportation roadside perception scenarios, the EA250 can be deployed in intersection cabinets, connecting multiple HD cameras and millimeter-wave radars to perform AI tasks locally in real time—including vehicle recognition, pedestrian detection, non-motor vehicle tracking, traffic event recognition, traffic flow statistics, and trajectory prediction. With 157 TOPS of compute, a single device can simultaneously process 8 channels of 1080P video streams, with object detection latency below 50 ms. It supports multi-sensor fusion algorithms combining radar and vision, improving object recognition accuracy by more than 20% compared with vision-only solutions, and operates stably in adverse conditions such as rain, fog, and nighttime. Inference results are sent directly to vehicles and traffic signal control systems via V2X links. The entire process is handled at the edge, with latency far lower than cloud solutions, meeting the low-latency requirements of vehicle-road cooperation.
Beyond intelligent transportation, the EA Series is equally suited to various outdoor industrial scenarios. In PV plants, it can serve as an edge computing node for drone inspection, identifying defects such as panel damage, hot spots, and dust accumulation locally in real time. In oil and gas field stations, it can be deployed for perimeter security AI analysis, identifying risks such as personnel intrusion, unauthorized operations, and leaks. In smart mines, it can be used for slope monitoring and equipment operation recognition to ensure safe mining production. For power supply, the wide-voltage input design adapts to unstable power scenarios such as outdoor PV + energy storage power supply and grid fluctuations on remote road sections. It operates normally within ±20% voltage fluctuation. With 5G module expansion, it enables efficient data collaboration between edge nodes and cloud platforms: the edge performs real-time inference and data preprocessing, while the cloud handles model iteration and big data analysis—balancing real-time performance and data value. For various outdoor scenarios, the EA Series—“industrial-grade hardware + full Jetson ecosystem”—enables edge AI to truly achieve “fits in, runs stably, performs well, and saves on O&M.”
