Deep Learning-based Framework for Shipping Container Security Seal Detection
Bibliographic record
Abstract
Shipping containers provide numerous benefits to global transportation. They are used to transport cargo from more than 30,000 cargo ships sailing across the world. The shipping containers provide the best protection of goods. This is because once all the goods are loaded into the container, it is sealed completely. The objective of the container seal is to minimize the risk of someone accessing the container and taking cargo out and avoid someone putting illegal stuff into the container such as drugs, weapons of mass destruction. To this end, shipping container terminals are required to inspect security seals when containers pass the gate of intermodal terminals. The existing detection mechanism is based on the human visual system which is time-consuming and hazardous. In this paper, a deep learning-based framework is proposed to automate shipping container security seal detection. The proposed method consists of three components including, handlers and cam keepers detection, handlers and cam keepers super-resolution regions, and security seal classification. For handlers and cam keepers detection you only look once (Yolov5) is employed to detect them with high performance. Following that, the laplacian pyramid super-resolution network (LapSRN) image super-resolution technique is used to convert low-resolution handlers and cam keepers regions to high-resolution sub-images. Finally, EfficientNetB0 is employed to classify the super-resolution sub-images based on two categories, seal or no-seal. The proposed whole security seal detection system is trained end-to-end that can localize and recognize the regions containing security seals with high performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".