FAQ: Cargo Analysis Questions Resolved

Not from every image. Capabilities depend on the imaging modality, cargo arrangement and validated task. Overlap, dense material and incomplete views can leave several plausible explanations. Even a useful finding may require other evidence before a reviewer can characterize the contents. See input preparation.

No. Optical images show visible surfaces; radiographic images encode attenuation through a load. They require different training data, preprocessing and interpretation skills. A model validated for exterior damage photographs cannot be assumed suitable for radiographic screening.

No. A score may not be a calibrated probability and can be unreliable on unfamiliar data. Even a calibrated probability describes evaluated behavior, not certainty about an individual shipment. Review the source, operating conditions and supporting evidence. See interpreting automated findings.

A model output does not itself confer authority to clear cargo. Any automated process must be assessed within the relevant legal and operational framework. This resource advocates a clear distinction between screening signals and authorized decisions, and provides no clearance functionality.

The workflow should record insufficient evidence and provide an authorized recapture or escalation path. Poor quality must not silently become a negative finding. Record the reason for rejection so recurring acquisition problems can be investigated.

Define whether the unit is a region, image or container, and state the reference process. Measure false alarms alongside missed findings, target prevalence, uncertainty and reviewer workload. A single aggregate accuracy percentage is rarely enough. See evidence-chain evaluation.

No. This website provides editorial information and a local text worksheet. It does not receive files for scanning, run detection models or evaluate shipments. Do not email sensitive imagery or operational security details in an editorial inquiry.

ISO 6346 supports container identification. WCO guidance, the NIST AI Risk Management Framework and relevant ISO governance standards provide other reference points. Applicability depends on the context. None independently validates a model or establishes the legal sufficiency of a decision.

Not automatically. Equipment, capture conditions, cargo populations and reviewer practices can change performance. A model evaluated in one location needs a justified local assessment before its results are relied on elsewhere, with monitoring after introduction.

Include the original image, source association, defined task, model output and version, known limitations, contextual records and an escalation route. The reviewer also needs competence, time and authority to challenge the system. See designing the human handoff.