An End-to-End Framework for Shipping Container Corrosion Defect Inspection

The worldwide transportation industry relies heavily on shipping containers. Containerization has made it easier to transfer goods all over the world by guaranteeing cargo safety while in transit. To ensure the safety of goods during transition, shipping containers should be reliable and kept in healthy conditions. Surface defect inspection of shipping containers is of great importance to guarantee the quality of containers. Customs officers must check surface of shipping containers as they pass through terminal gates during the transition. Human visual observation is the basis for the current inspection method, which is time-consuming, labor-intensive, and possibly hazardous. The purpose of this research is to present a deep learning-based framework that can be used in conjunction with a computer vision technique to successfully and efficiently inspect corrosion defects on the surface of shipping containers. The proposed framework consists of two main modules, including High-resolution and Temporal Context R-CNN (HRTC R-CNN) for corrosion defect detection and Corrosion Defect Characterization (CDC) for corrosion defect inspection. HRTC R-CNN uses multi-stream backbone, multi-depth and multi-scale super resolved feature generation to extract semantic information from different defect scales. The shallow network receives high-resolution images to maintain positional information, while the deep network receives low-resolution images to extract semantic information. In order to increase the framework’s performance, attention mechanism and two memory banks are used to leverage context information from unlabeled images. In CDC, an optical flow based image stitching is proposed to calculate the percentage of corrosion on the surface of the whole container. Experiments on corrosion defect dataset demonstrate the excellent accuracy and resilience of our approach. This technology will expedite the process of container’s defect inspection at terminals, thereby supporting in container transport logistics and supply chain process.