Intelligent Container Damage Detection

During the loading, unloading, and transportation of shipping containers, a certain degree of damage may occasionally occur. For those responsible for container storage and management, accurately assessing the condition of incoming containers is crucial for preventing unnecessary complications and disputes.

Container damage detection systems were developed specifically to address this need. By utilizing machine vision technology to analyze the container body under inspection, these systems can determine whether any damage is present on the container's surface.

The workflow for container damage detection is as follows

High-definition network cameras (IPCs) capture images of the container body to analyze it for any potential issues.

Once the container has moved past the inspection point, the captured images of its surfaces are stitched together to form a composite view, facilitating easier observation and analysis, and generating a final damage assessment report.

Both the captured images and the damage assessment results are automatically saved to a database.

Damage Detection Success Rate: Through machine vision inspection, the overall success rate for detecting damage reaches approximately 95%.

Damage Type Classification Rate: The success rate for accurately identifying and classifying the specific *type* of damage is approximately 90%.

Core Technologies for Container Damage Detection

Image Preprocessing: Images undergo preprocessing—including median filtering, sharpening, and binarization—to analyze their texture. This texture data is then matched against a built-in library of damage-specific textures to determine whether any damage is present.

Extensible Interface: The system features an interface that supports a "new damage texture learning" function, allowing for continuous improvement in recognition and assessment accuracy over time.

Neural Network Classification: A neural network is employed to learn and classify various types of damage, thereby enabling the system to accurately categorize the specific nature of the detected damage.