Smart Port/
Storage and TransportationPort TerminalThe average accuracy is ≥90%, and by leveraging deep learning technology, the performance can be continuously improved through iterations. On average, each algorithm covers more than 500 specific scenarios, with 500-800 precisely annotated images or pieces/segments of data (depending on the type) for each scenario, ensuring the quality of the algorithm from the source of the data.
Customization is based on actual needs, typically requiring between 2,000 and 5,000 image samples for each scenario. The customization process includes: solution design → data collection → data annotation → algorithm development → algorithm testing → algorithm iteration.
A standard 2 million pixel (2MP) camera is sufficient. The solution supports the reuse of existing cameras and video stream access via RTSP, RTMP, GB28181, etc., which helps in saving renovation costs.
The algorithm supports deployment on local servers, edge devices, and cloud servers. Edge deployment is compatible with edge computing boxes from Huawei Ascend, Sanechips, Rockchip, Intel, and others, ready for immediate use without additional setup.
Pricing for the algorithm is flexible and tailored to actual needs and scale. The main pricing models include per-channel licensing, server licensing, and annual package licensing.
Detect whether the ship is docking or departing
After the cable is tied to the mooring pier and stretched straight, check whether there is anyone near the cable and the mooring pier.
Checking whether the personnel are in danger of falling into the water.
