Deep Learning-based Framework for Shipping Container Security Seal Detection

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.