Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67797
Volume:
10 |
10 |
Oct 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Mechanical Engineering
Priyadarshani College of Engineering
Mechanical Engineering
Priyadarshani College of Engineering
Mechanical Engineering
Priyadarshani College of Engineering
Mechanical Engineering
Priyadarshani College of Engineering
Mechanical Engineering
Priyadarshani College of Engineering
Mechanical Engineering
Priyadarshani College of Engineering
Crack detection is an important requirement in the industrial quality inspection as the undetected surface cracks can propagate under the service loading and lead to the component failure. Conventional visual inspection is time consuming and is dependent on the operator experience, lighting conditions and crack visibility. We report the development and experimental validation of a low-cost camera based automated crack detection system for industrial components. The proposed system consists of an ESP32-CAM for image acquisition, an RFID-RC522 module for component identification, a conveyor mechanism for automatic component transportation, image-processing algorithms for surface-crack detection and pneumatic mechanism for automatic component sorting. Mild-steel components are passed through an inspection zone in which surface images are obtained under controlled illumination. The pneumatic mechanism segregates defective components from the acceptable components on the basis of the detection decision. The developed prototype offers an integrated approach to the automated visual inspection and reduces the reliance on continuous manual inspection. Experimental testing is conducted to evaluate the crack-detection accuracy, precision, recall, processing time and sorting capability in different crack conditions. The results demonstrate the feasibility of the proposed low-cost system for automated surface-defect inspection, and also provide a basis for further development with higher-resolution imaging and sophisticated machine-learning-based detection techniques.
KEYWORDS
The developed camera-based automated crack detection system offers a systematic approach to identify surface cracks in industrial components and to reduce reliance on repetitive manual inspection. The prototype consists of a conveyor belt, a 12 V 60 RPM DC motor, a chain and sprocket mechanism, an ESP32-CAM, RFID-RC522 reader, relay, compressor and a pneumatic cylinder. The motor moves the conveyor, which carries the components through the inspection zone. The ESP32-CAM captures images of the surface of the components at an appropriate viewing angle. The acquired images are processed to differentiate between cracked and sound surfaces and the pneumatic mechanism can be used for component separation. The system is highly dependent on the image quality, illumination, surface condition of components and reliability of image processing method
[1] Ni, F.; Zhang, J.; Chen, Z. Pixel level crack delineation in images with convolutional feature fusion. Struct. Control. Health Monit. 2019, 26, e2286. [2] Liong, S.T.; Gan, Y.S.; Huang, Y.C.; Yuan, C.A.; Chang, H.C. Automatic defect segmentation on leather with deep learning. arXiv 2019, arXiv:1903.12139. [3] Zhang, L.; Yang, F.; Zhang, Y.D.; Zhu, Y.J. Road crack detection using a deep convolutional neural network. In Proceedings of the International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 25–28 September 2016; pp. 3708–3712 [4] Pauly, L.; Peel, H.; Luo, S.; Hogg, D.; Fuentes, D.H.A.R. Deeper networks for pavement crack detection. In Proceedings of the 34th ISARC, Taipei, Taiwan, 28 June–1 July 2017; pp. 479–485. [5] Yang, X.; Li, H.; Yu, Y.; Luo, X.; Huang, T.; Yang, X. Automatic pixel-level crack detection and measurement using a fully convolutional network. Comput. Aided Civ. Infrastruct. Eng. 2018, 33, 1090–1109. [6] Salman, M.; Mathavan, S.; Kamal, K.; Rahman, M. Pavement crack detection using the Gabor filter. In Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, The Netherlands, 6–9 October 2013; pp. 2039–2044. [7] Zhang, A.; Wang, K.C.P.; Li, B.; Yang, E.; Dai, X.; Peng, Y.; Fei, Y.; Liu, Y.; Li, J.Q.; Chen, C. Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network. Comput. Aided Civ. Infrastruct. Eng. 2017, 32, 805–819. [8] Dorafshan, S.; Thomas, R.J.; Maguire, M. Fatigue crack detection using unmanned aerial systems in fracture critical inspection of steel bridges. J. Bridge Eng. 2018, 23, 04018078. [9] Dorafshan, S.; Thomas, R.J.; Maguire, M. Benchmarking image processing algorithms for unmanned aerial system-assisted crack detection in concrete structures. Infrastructures 2019, 4, 19. [ [10] Zhu,J.; Ren, M.W.; Yang, Z.J.; Zhao, W. Fast matching algorithm based on corner detection. J. Nanjing Univ. Sci. Technol. 2011, 35, 755–758.
Publication Date
10/10/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
1
Total views since publication
PDF Downloads
11
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...