The invention provides a steel surface defect detection and
identification system and method based on a
deep learning YOLOv8 model, and the
system comprises an industrial camera which is disposed in a to-be-detected region of a steel
production line and is provided with a high-brightness
light source. The
system is characterized by further comprising an
image acquisition module, a fault detection module, a defect identification module and a man-
machine interaction module which run on a computer and are connected with the industrial
camera control system, and the method comprises the following steps: S1, system initialization, S2,
image acquisition, S3, image preprocessing, S4,
feature extraction and fusion, S5, defect identification and classification, and S6, result output and interaction. Automatic and precise steel surface defect detection and recognition of the whole process from
image acquisition, defect detection to result output are achieved, manual visual judgment is not needed, detection standards are unified, the problem of unstable detection caused by manual experience differences is avoided, the labor intensity of operators is reduced, enterprise losses caused by defective steel circulation are avoided, and the production efficiency is improved. Reliable data support is provided for production
process optimization, and the
steel quality is guaranteed.