Internal defects detection and classification in hollow cylindrical surfaces using single shot detection and MobileNet

Surface defect inspection, detection, and classification in hollow cylindrical surfaces such as pipes and barrels have a significant impact on the structural integrity of various industrial products. Regular inspection and identification of the faults reduces the likelihood of faults' aggravation, limits the damaging effects, and increases the product life. However, most of the defect detection algorithms for cylindrical surfaces rely heavily on handcrafted feature extraction limiting the ability to recognize the defects effectively. This research work proposes an image processing-based automatic defect detection and classification approach for cylindrical hollow surfaces. The proposed system uses a single shot multi-box detection (SSD) algorithm for localization and a customized lightweight deep convolutional neural network as a backbone network to classify defects generally found in industrial pipes and gun barrels. First, the image dataset is acquired from a real-time working environment using an indigenously developed borescope featuring a rotating camera and special hardware features. Later, the bounding boxes are calculated using extracted features to localize defects with SSD which takes a single shot to detect multiple objects within the image. Finally, the defected regions are classified into five classes of commonly found issues of pitting, chipping, rusting, dirt, and thermal cracking by utilizing deep learning architecture of 53 layers. It is found that the proposed approach can indicate the exact location of the classified defect in terms of angle and distance from a reference point. Also, the proposed method improves the detection and classification accuracy significantly compared to other existing methods. To encourage the development and evaluation of new methods for cylindrical surface defect detection, the dataset is also made publicly available.