Detection of Damages in Wire Ropes Using Computer Vision Techniques

Wire ropes are one of the most commonly used objects in industries and at construction sites. These are used to lift heavy objects and are part of cranes. Wire ropes are constantly exposed to all forms of nature such as heat from the sun and the humidity from the rain, additionally, wire ropes lift very heavy weights, which makes them prone to damage. If these damages were gone without notice, they may cause severe accidents. Thus, this study was performed to develop a model that can detect the damages in wire ropes using the deep learning algorithm YOLOv8. YOLOv8 is a computer vision-based system that is precisely known for its best object detection skills. The model was trained using a hybrid dataset consisting of 2892 images. The model showed an accuracy of 98.31%. This developed model using YOLOv8 is reliable for real-time detection of wire rope damage.