FAECCD-CNet: Fast Automotive Engine Components Crack Detection and Classification Using ConvNet on Images

Abstract

Crack inspections of automotive engine components are usually conducted manually; this is often tedious, with a high degree of subjectivity and cost. Therefore, establishing a robust and efficient method will improve the accuracy and minimize the subjectivity of the inspection. This paper presents a robust approach towards crack classification, using transfer learning and fine-tuning to train a pre-trained ConvNet model. Two deep convolutional neural network (DCNN) approaches to training a crack classifier—namely, via (1) a Light ConvNet architecture from scratch, and (2) fined-tuned and transfer learning top layers of the ConvNet architectures of AlexNet, InceptionV3, and MobileNet—are investigated. Data augmentation was utilized to minimize over-fitting caused by an imbalanced and inadequate training sample. Data augmentation improved the accuracy index by 4%, 5%, 7%, and 4%, respectively, for the proposed four approaches. The transfer learning and fine-tuning approach achieved better recall and precision scores. The transfer learning approach using the fine-tuned features of MobileNet attained better classification accuracy and is thus proposed for the training of crack classifiers. Moreover, we employed an up-to-date YOLOv5s object detector with transfer learning to detect the crack region. We obtained a mean average precision (mAP) of 91.20% on the validation set, indicating that the model effectively distinguished diverse engine part cracks.

Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

“…Its purpose is commonly illustrated as an effective, efficient object detection, recognition, and classification application with fewer error rates. The detector has been applied to face mask recognition [ 48 , 49 ], real-time vehicle detection [ 50 ], vehicle classification [ 51 ], off-road quad-bike detection [ 52 ], pedestrian detection [ 53 ], medical image classification [ 54 ], automotive engine crack detection [ 55 ] and so on.…”

Detecting Machining Defects inside Engine Piston Chamber with Computer Vision and Machine Learning

“…A drawback of this approach would be the high processing time of the high-resolution input image and the volatile environment from where the image is acquired, which will lead to a repeatability issue in the image due to dynamic shapes and contrast of the emulsion marks [ 22 ].…”

“…The human–machine interface was implemented using the PyQt framework (PyQt5-Qt5 version 5.15.2, developed by Riverbank Computing, open-source), which is a Python wrapper of the open-source framework Qt developed by the Qt company. Software was designed to cover all of the manufacturing necessities, e.g., logging, user management, process handling, and so on [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. In Figure 6 , a complete sequence diagram of the process can be observed.…”

“…When the emulsion becomes mixed with dust, traces become increasingly noticeable and with a lower grayscale level. Because of that, the thresholding is no longer able to distinguish between traces of emulsion and actual defects [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ].…”

“…In Figure 7 , an architecture of the neural network is proposed [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ].…”