Synthetic Lifting Rope Damage Detection Using CNN Vision

The patent replaces electromagnetic and radiation-based detection methods with optical imaging and computer vision technology. Instead of using electromagnetic fields or radiation to detect damage, the system uses cameras to capture images of the rope surface and processes these images through neural networks to identify damage, making the detection method compatible with synthetic materials.

The patent changes the detection parameters from electromagnetic properties to visual/optical properties. By shifting from detecting electromagnetic responses to analyzing visual characteristics like color, texture, and pattern changes in the rope surface, the system becomes applicable to synthetic ropes while maintaining damage detection capability.

The patent performs preliminary actions by pre-processing images before they are fed into the neural network. This includes resizing images to standardized dimensions, normalizing pixel values, and applying data augmentation techniques during the training phase. These preliminary steps prepare the data in advance, reducing the computational burden during real-time inference while maintaining detection accuracy.

The patent applies partial action by selectively processing only the most relevant features of the images. The neural network is trained to focus on specific visual indicators of damage rather than processing every pixel equally, and the system may discard less important image data after initial processing, reducing computational requirements while retaining essential detection capabilities.