Machine Learning Based Dent Damage Detection Using Image Processing Methods

Finding cracks and dents in pressed panel goods is a crucial step in assessing their quality. This study presents a novel edge line evaluation method for non-invasive crack detection based on obtaining the captured object's outline. The objective of this proposed work is to use convolutional neural network (CNN) algorithm to develop an automated system that can accurately recognize dents on cars or other metal surfaces. The suggested system uses YOLO V3, R-CNN algorithms, and image processing approaches to find dents or fissures in metal or concrete surfaces. A competitive edge should prioritize improved accuracy, quicker dent detection, wider applicability, and cost-effectiveness.