Deteksi Objek Pada Framework Yolov5 Dengan Penanganan Kesilauan Cahaya Menggunakan Gabungan Arsitektur U-Net Dan Inpaint

Deteksi Objek Pada Framework Yolov5 Dengan Penanganan Kesilauan Cahaya Menggunakan Gabungan Arsitektur U-Net Dan Inpaint

Penulis

  • Firman Afrialdy Universitas Brawijaya
  • Rizal Setya Perdana
  • Candra Dewi

Kata Kunci:

deteksi objek, segmentasi objek, deep learning, yolov5, inpaint

Abstrak

Naskah ini akan diterbitkan di JTIIK.

Referensi

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Guo, F. and Xu, Y.,. Vehicle Analysis System Based on DeepSORT and YOLOv5. In: 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA). pp.175–179. https://doi.org/10.1109/CVIDLICCEA56201.2022.9824363.

Huang, S., He, Y. and Chen, X., 2021. MYOLO: A Nighttime Vehicle Detection Method Combining Mobilenet v2 and YOLO v3. Journal of Physics: Conference Series, [online] 1883(1), p.012094. https://doi.org/10.1088/17426596/1883/1/012094.

Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., NanoCode012, Kwon, Y., Michael, K., TaoXie, Fang, J., imyhxy, Lorna, 曾逸夫(Zeng Yifu, Wong, C., Abhiram V, Montes, D., Wang, Z., Fati, C., Nadar, J., Laughing and UnglvKitDe, 2022. ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation. https://doi.org/10.5281/zenodo.7347926.

Kutlimuratov, A., Khamzaev, J., Kuchkorov, T., Anwar, M.S. and Choi, A., 2023. Applying Enhanced RealTime Monitoring and Counting Method for Effective Traffic Management in Tashkent. Sensors, 23(11). https://doi.org/10.3390/s23115007.

Mahaur, B. and Mishra, K.K., 2023. Smallobject detection based on YOLOv5 in autonomous driving systems. Pattern Recognition Letters, [online] 168, pp.115–122. https://doi.org/10.1016/j.patrec.2023.03.009.

Miao, Y., Liu, F., Hou, T., Liu, L. and Liu, Y., 2020. A nighttime vehicle detection method based on YOLO v3. pp.6617–6621. https://doi.org/10.1109/CAC51589.2020.9326819.

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Parvin, S., Islam, M.E. and Rozario, L.J., 2022. Nighttime Vehicle Detection Methods Based on Headlight Feature: A Review. IAENG International Journal of Computer Science, 49(1).

Ronneberger, O., Fischer, P. and Brox, T., 2015. U-net: Convolutional networks for biomedical image segmentation. CoRR, [online] abs/1505.04597. Available at: <http://arxiv.org/abs/1505.04597>.

Telea, A., 2004. An Image Inpainting Technique Based on the Fast Marching Method. Journal of Graphics Tools, 9. https://doi.org/10.1080/10867651.2004.10487596.

Vinoth, K. and P, S., 2024. Lightweight Object Detection in Low light: Pixelwise Depth Refinement and TensorRT Optimization. Results in Engineering, [online] 23(102510). https://doi.org/10.1016/j.rineng.2024.102510.

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Diterbitkan

24 Okt 2024

Cara Mengutip

Afrialdy, F., Setya Perdana, R., & Dewi, C. (2024). Deteksi Objek Pada Framework Yolov5 Dengan Penanganan Kesilauan Cahaya Menggunakan Gabungan Arsitektur U-Net Dan Inpaint. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 8(13). Diambil dari https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/14218
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