Semantic Segmentation of Corrosion in Cargo Containers Using Deep Learning

As global trade expands, the pressure on container terminals to improve efficiency and capacity grows. Several inspections are performed during the loading and unloading process to minimize delays. In this paper, we explore corrosion as it poses a persistent threat that compromises the durability of containers and leads to costly repairs. However, identifying this threat is no simple task. Corrosion can take many forms, progress unpredictably, and be influenced by various environmental conditions and container types. In collaboration with the Port of Sines, Portugal, this work explores a potential solution for a real-time computer-vision system, with the aim to improve container inspections using deep-learning algorithms. We propose a system based on the semantic segmentation model, DeepLabv3+, for precise corrosion detection using images provided from the terminal. After preparing the data and annotations, we explored two approaches. First, we leveraged a pre-trained model originally designed for bridge corrosion detection. Second, we fine-tuned a version specifically for cargo container assessment. With a corrosion detection performance of 49%, this work showcases the potential of deep learning to automate inspection processes. It also highlights the importance of generalization and training in real-world scenarios and explores innovative solutions for smart gates and terminals.