Surface defect detection plays a pivotal role in ensuring product quality in industrial production, as defects like cracks, scratches, and dents can compromise product performance and durability. Traditional detection methods, such as manual inspection and Non-Destructive Testing (NDT), are limited by inefficiency, reliance on human expertise, and susceptibility to errors, which restrict their application in large-scale production. With advancements in artificial intelligence, deep learning models, particularly Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), have emerged as promising solutions for automated surface defect detection. This paper provides a comprehensive review of surface defect detection technologies, starting from traditional methods to modern deep learning-based techniques. The advantages and limitations of each approach are analyzed, highlighting key advancements in deep learning, including recent models like Faster R-CNN, Cascade R-CNN, and YOLOv4. Furthermore, challenges such as handling complex defects and improving detection accuracy in real-world industrial environments are discussed, along with potential directions for future research. Experimental evaluations using the Few Steels Classification (FSC) dataset demonstrate the effectiveness of modern detection methods in industrial applications, offering insights into enhancing defect detection systems.