Published: September 17, 2026 | Category: Application Guide | QSCompute
A container terminal is a camera problem wearing an industrial disguise. Every box that enters the gate, is lifted by a crane, crosses the yard or boards a train has its identifier read, its condition inspected and its position tracked. Doing that with people is slow and gets slower as throughput rises; doing it with inference means putting GPU-class compute on moving steel, in salt air, on a quay.
That combination — a vision workload plus a genuinely hostile installation environment — is what makes terminal automation a hardware selection problem rather than a software one. The models are mostly mature. The enclosures, power and thermal design are not.
The workloads differ enough in camera count and frame rate that a single "terminal GPU" specification is meaningless. Size each location separately, then consolidate where the cabling allows it.
| Application | Typical sensor load | Inference character | Placement |
|---|---|---|---|
| STS crane container ID (OCR) | 2–4 cameras per crane leg | Fast detection plus OCR on every lift | On-crane cabinet |
| Spreader and twistlock confirmation | 2–4 cameras at the spreader | Small models, hard real-time | On-crane, vibration-critical |
| RTG / RMG anti-collision and gantry alignment | 4–8 cameras along the gantry | Continuous detection and ranging fusion | On-machine |
| Truck gate OCR and damage inspection | 4–8 lanes, multi-camera per lane | Bursty — load scales with gate traffic | Gate house or nearby kiosk |
| Rail gate and wagon number reading | 2–6 cameras per track | Detection plus sequence matching | Wayside cabinet |
| Yard visibility and trailer tracking | Pole-mounted PTZ, dozens of views | Many low-rate streams, one shared pool | Yard aggregation node |
| Dangerous goods and placard detection | 1–2 cameras per lane | Second-stage classifier on gate output | Same node as gate OCR |
Most terminals end up with two or three tiers rather than one uniform machine, because a spreader cabinet and a yard aggregation node have almost nothing in common except a model runtime.
| Tier | Representative hardware class | Streams / workload | Notes |
|---|---|---|---|
| Embedded accelerator | Module-class edge AI hardware, 25–100 TOPS, fanless | 1–3 streams, single application | Best fit for spreader and small cabinets |
| Mid-tier GPU | 60–120 W add-in GPU in a sealed industrial PC | 4–12 decoded streams | Sweet spot for crane cabinets and rail gates |
| Multi-GPU node | Industrial server with 2–4 GPUs | 20–60 streams, model ensemble | Gate houses and yard aggregation |
| Central GPU server | Rack-scale, fed by fibre from cameras | Whole-terminal, model retraining | Only where cabling and latency allow |
Centralising everything is tempting and usually wrong at the crane. Decoding a multi-megapixel stream at 30 fps and shipping it across a swing-radius fibre run costs more bandwidth than the inference itself, and it makes a network fault into a safety event. Push inference to the asset and send metadata back.
A gate kiosk and a crane cabinet sit in the same terminal and face different physics. This is the part of terminal automation that ordinary industrial PC selection guides do not cover.
| Stress | Quay / crane side | Gate / yard side | Design response |
|---|---|---|---|
| Salt-laden humidity | Continuous, year round | Moderate | Marine-grade coating, sealed IP-rated enclosure, no unfiltered fans |
| Vibration | High, machine-mounted | Low | Locking connectors, shock-mounted drives, no cable-mounted cards |
| Temperature | Direct sun to cold nights | Solar gain on kiosks | Wide-temperature SSD and DRAM, fanless where possible |
| Power quality | Spikes from hoist and trolley drives | Cleaner mains | Isolated supply, wide-input DC-DC, UPS or supercap ride-through |
| Maintenance access | Difficult, scheduled with operations | Moderate | Watchdog, A/B firmware, remote management from day one |
Two details cause a disproportionate share of field failures. The first is a storage device rated for a comfortable office rather than a quay, which begins returning read errors a little over a year in. The second is a GPU whose thermal design assumed a rack with airflow, installed in a sealed cabinet where it throttles down within minutes of the shift starting.
Crane cabinets are fed from the machine's own DC bus, which is not clean. Treat the supply as hostile: specify wide-input DC-DC conversion with transient protection, and confirm the input range against measured sag during hoist start rather than against the nameplate. Budget for ride-through so that a brief dip does not reboot the vision system mid-lift.
For RTGs and other rubber-tyred machines, there is no fibre path at all and the link is wireless. That changes the architecture: inference runs locally, only boxes and events cross the air, and the local node must keep operating — buffering results — through an outage of minutes rather than seconds.
QSCompute supplies the industrial GPU nodes, sealed edge enclosures and wide-temperature storage used at crane, gate and yard positions, with marine-grade finishes, expanded DC input ranges and A/B update support. We ship terminals and ports worldwide with documented environmental ratings and long-lifecycle supply.
Automating a terminal or rail gate?
Send us your camera layout, ambient envelope and stream counts — our engineers return a compute tier per location with thermal, power and enclosure specifications documented.
Contact: +86 137-1464-6179 | info@qscompute.com