During the production of armatures, many types of defects in its structure arise, making effective quality assurance techniques necessary. We have proposed a CNN-Autoencoder with linear regression-based image analyzer for detecting defective armatures using a dataset of over 500 images. CNN was used for feature extraction and reducing data dimensions. An autoencoder will detect anomalies using the perfect armature pieces segregating the defective ones. Lastly, a linear regression layer has been developed for classification based on intra-variations in the image distribution. The features for the regression model were generated using various statistical techniques in which the intra-image variation and distribution was considered. The F1-score achieved was 80.01%.