Detects Damage Car Body using YOLO Deep Learning Algorithm
Keywords:Deep Learning, Detection, , Cracks, Car Body, Damage, Scratches
This journal presents a technique for detecting scratches, cracks and other damage to car bodies using machine learning methods. This method is used to improve process efficiency and checking accuracy and can also reduce the cost and time required for manual inspection. The method includes collecting image datasets of cars in good and damaged condition, followed by preprocessing and segmentation to separate damaged or damaged car parts. not broken. Then, it is followed by a deep learning algorithm, namely You Only Look Once, or Faster Region-based Convolutional Neural Networks, which is used to build a detection model. The model is trained and tuned using the collected data, then evaluated using the test data to measure the accuracy and precision of the detection results. The experimental results show that the proposed method achieves high accuracy and efficiency in detecting scratches, cracks, and other defects on the car body, with precision of an average of more than 70%. This method provides a promising approach to improving the car body inspection process which can be used by taxi companies to help inspect and maintain vehicles more quickly and accurately, to help with insurance, avoid accidents and so on.
Bipin Nair, B. J., Aadith Raj, K., Kedar, M., Vaishak, S. P., & Sreejil, E. (2023). Ancient Epic Manuscript Binarization and Classification Using False Color Spectralization and VGG-16 Model. Procedia Computer Science, 218, 631–643. https://doi.org/10.1016/j.procs.2023.01.045
Chirgaiya, S., & Rajavat, A. (2023). Tiny object detection model based on competitive multi-layer neural network (TOD-CMLNN). Intelligent Systems with Applications, 18(September 2022), 200217. https://doi.org/10.1016/j.iswa.2023.200217
Hadianto, E., Amanda, D., Hindarto, D., Makmur, A., & Santoso, H. (2023). Design and Development of Coffee Machine Control System Using Fuzzy Logic. Sinkron, 8(1), 130–138. https://doi.org/10.33395/sinkron.v8i1.11917
Hindarto, D., Indrajit, R. E., & Dazki, E. (2021). Sustainability of Implementing Enterprise Architecture in the Solar Power Generation Manufacturing Industry. Sinkron, 6(1), 13–24. https://jurnal.polgan.ac.id/index.php/sinkron/article/view/11115
Hindarto, D., & Santoso, H. (2021). Plat Nomor Kendaraan dengan Convolution Neural Network. Jurnal Inovasi Informatika, 6(2), 1–12. https://doi.org/10.51170/jii.v6i2.202
Hindarto, D., & Santoso, H. (2022). PERFORMANCE COMPARISON OF SUPERVISED LEARNING USING NON-NEURAL NETWORK AND NEURAL NETWORK. Janapati, 11, 49–62.
Kong, L., Wang, J., & Zhao, P. (2022). YOLO-G: A Lightweight Network Model for Improving the Performance of Military Targets Detection. IEEE Access, 10, 55546–55564. https://doi.org/10.1109/ACCESS.2022.3177628
Lee, Y., Yun, J., Hong, Y., Lee, J., & Jeon, M. (2018). Accurate License Plate Recognition and Super-Resolution Using a Generative Adversarial Networks on Traffic Surveillance Video. 2018 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2018, June 2018, 1–4. https://doi.org/10.1109/ICCE-ASIA.2018.8552121
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 779–788. https://doi.org/10.1109/CVPR.2016.91
Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, faster, stronger. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 6517–6525. https://doi.org/10.1109/CVPR.2017.690
Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031
Rostianingsih, S., Setiawan, A., & Halim, C. I. (2020). COCO (Creating Common Object in Context) Dataset for Chemistry Apparatus. Procedia Computer Science, 171(2019), 2445–2452. https://doi.org/10.1016/j.procs.2020.04.264
Saputra, A. D., Hindarto, D., & Santoso, H. (2023). Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201. Sinkron, 8(1), 48–55. https://doi.org/10.33395/sinkron.v8i1.11906
Sze, E., Santoso, H., & Hindarto, D. (2022). Review Star Hotels Using Convolutional Neural Network. 7(1), 2469–2477.
Tong, K., & Wu, Y. (2023). Rethinking PASCAL-VOC and MS-COCO dataset for small object detection. Journal of Visual Communication and Image Representation, 103830. https://doi.org/10.1016/J.JVCIR.2023.103830
van Ruitenbeek, R. E., & Bhulai, S. (2022). Convolutional Neural Networks for vehicle damage detection. Machine Learning with Applications, 9(December 2021), 100332. https://doi.org/10.1016/j.mlwa.2022.100332
Wang, S., Xia, X., Ye, L., & Yang, B. (2021). Automatic detection and classification of steel surface defect using deep convolutional neural networks. Metals, 11(3), 1–23. https://doi.org/10.3390/met11030388
Wedha, B. Y., Helmi, H., Dazki, E., & Indrajit, R. E. (2022). Adopsi IoT Pada Core Process Trucking di Indonesia Dengan Menggunakan TOGAF Framework. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(1), 230–243. https://doi.org/10.35957/jatisi.v9i1.1980
Wedha, B. Y., Karjadi, D. A., Wedha, A. E. P. B., & Santoso, H. (2022). Style Transfer Generator for Dataset Testing Classification. SinkrOn, 7(2), 448–454. https://doi.org/10.33395/sinkron.v7i2.11375
Xue, G., Li, S., Hou, P., Gao, S., & Tan, R. (2023). Research on lightweight Yolo coal gangue detection algorithm based on resnet18 backbone feature network. Internet of Things (Netherlands), 22(March), 100762. https://doi.org/10.1016/j.iot.2023.100762
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Copyright (c) 2023 Yonathan Wijaya Gustian, Ben Rahman, Djarot Hindarto, Alessandro Benito Putra Bayu Wedha
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