Aim: This paper studies to detect the undetectable defects in casting such as air holes, pinholes, burrs, tensile defects, mold material defects, metal casting defects, metallurgical defects, and etc. Through images via deep learning methods. Method: In the study, an automatic reading review for submersible pump impeller is proposed and a deep learning model is developed. The data set of the images include defective and smooth submersible pump impeller has been utilized to prove the performance of the designed network architecture. Results: According to the obtained results, maximum accuracy of 89% for the classifier has been achieved in the training stage and it reached a maximum accuracy of 93% in the testing stage. Conclusion: Along with the developing systems, quality control systems have been evolved through image processing. Thanks to the image processing-based control systems, the capacity and efficiency of the production facilities can be increased and perfect products can be delivered to the end-user by making precise measurements. In this study, since casting is an important and frequently used process in the industry, its defects are evaluated and a deep learning model to automate the review process and detect the defective products of submersible pump impellers is presented. The success rate of 89% obtained show that the defective product inspection in the industry can be performed over the images by using a convolutional neural network (CNN) architecture.