Detecting surface defects in steel manufacturing is crucial for product quality and production efficiency. However, real-time quality control faces challenges in automation and reliability. Surface flaws in steel strips vary in complexity, requiring robust defect detection algorithms with high generalization performance.For the purpose of addressing this issue, we visited JSW Steel Ltd, Vijayanagara, Ballari, Karnataka, India, and took pictures of any defects we found. Using deep convolutional neural networks (CNNs), we provide a new method for steel defect identification. Images of surface defects in steel were used to train and evaluate the deep CNN. This nine-layer CNN model solves the problem of finding flaws in steel strips as a whole. Implementation of data augmentation techniques to avoid overfitting. The performance of the proposed model was assessed using a dataset that included three classes of flaws and one class that was free of flaws. On the validation set, the model achieved an impressive accuracy of 93.27 percent. The experimental results demonstrate the deep CNN model’s higher performance for both intra and inter-class fault detection. So, the proposed deep CNN model gives a precise and real-time way to find surface flaws in steel strip production lines, leading to higher-quality steel strips overall.