Peningkatan Deteksi Kecelakaan di Jalan Raya Menggunakan Real-ESRGAN pada Citra CCTV Persimpangan Jalan

  • Muhammad Fachry Ikhsal Universitas Singaperbangsa Karawang
  • Budi Arif Dermawan Universitas Singaperbangsa Karawang
  • Riza Ibnu Adam Universitas Singaperbangsa Karawang
Keywords: Transfer Learning, MobileNetV2, Real-ESRGAN, Accident Detection


The failure of the accident detection system on CCTV cameras can affect the increase in the death rate on the highway. The use of the CNN method in the construction of CCTV accident detection systems has been widely used before. However, common problems that are often encountered are dirty lenses and varifocal zooms that don't automatically focus, causing the quality of the resulting CCTV images to decrease, thus affecting system performance. In this research, a model was developed to detect accidents on CCTV images using the MobileNetV2 pre-trained model which was optimized by upscaling the dataset using the Real-ESRGAN model to produce more optimal performance. This study uses a CCTV image dataset totaling 989 and consisting of 2 types of prediction classes including accident and non-accident. The results showed that the MobileNetV2 model succeeded in producing 94% testing accuracy and an average inference time of 3.33 seconds in the GT test scenario. During the testing process, it was found that the model was not optimal if it identified new data with clustered objects. In addition, based on the test scenarios X2, X4, X8 it was found that the image quality calculated based on PSNR and SSIM values greatly influences classification performance such as accuracy, precision, recall, and AUC score.


Download data is not yet available.


P. N. Megasari, “Polri Catat 6.707 Kasus Kecelakaan Sepanjang 2022, 452 Orang Tewas,” detikNews, 2022. (accessed Apr. 25, 2023).

S. Ghosh, S. J. Sunny, and R. Roney, “Accident Detection Using Convolutional Neural Networks,” in 2019 International Conference on Data Science and Communication (IconDSC), 2019, pp. 1–6. doi: 10.1109/IconDSC.2019.8816881.

C. B. Ng and W. H. Lo, “Effect of Image Distortion on Facial Age and Gender Classification Performance of Convolutional Neural Networks,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, 2019. doi: 10.1088/1757-899X/495/1/012029.

H. Triana and U. Enri, “Penerapan Deep Learning Pada Kamera Pengawas Jalan Raya Dalam Mendeteksi Kecelakaan,” 2022.

J. Pardede and H. Hardiansah, “Deteksi Objek Kereta Api menggunakan Metode Faster R-CNN dengan Arsitektur VGG 16,” MIND Journal, vol. 7, no. 1, pp. 21–36, Jun. 2022, doi: 10.26760/mindjournal.v7i1.21-36.

C. A. Hartanto and A. Wibowo, “Development of Mobile Skin Cancer Detection using Faster R-CNN and MobileNet v2 Model,” in 2020 7th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), 2020, pp. 58–63. doi: 10.1109/ICITACEE50144.2020.9239197.

A. G. Howard et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” Apr. 2017, [Online]. Available:

R. Indraswari, R. Rokhana, and W. Herulambang, “Melanoma Image Classification Based on MobileNetV2 Network,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 198–207. doi: 10.1016/j.procs.2021.12.132.

A. Horé and D. Ziou, “Image Quality Metrics: PSNR vs. SSIM,” in 2010 20th International Conference on Pattern Recognition, 2010, pp. 2366–2369. doi: 10.1109/ICPR.2010.579.

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Jan. 2018, [Online]. Available:

S. Afaq and S. Rao, “Significance of Epochs on Training A Neural Network,” 2020, [Online]. Available:

How to Cite
M. Ikhsal, B. Dermawan, and R. Adam, “Peningkatan Deteksi Kecelakaan di Jalan Raya Menggunakan Real-ESRGAN pada Citra CCTV Persimpangan Jalan”, JAIC, vol. 7, no. 1, pp. 57-62, Jul. 2023.