Deteksi Objek Pada Framework Yolov5 Dengan Penanganan Kesilauan Cahaya Menggunakan Gabungan Arsitektur U-Net Dan Inpaint
Kata Kunci:
deteksi objek, segmentasi objek, deep learning, yolov5, inpaintAbstrak
Naskah ini akan diterbitkan di JTIIK.
Referensi
Bazzi, A., Murthy, J.S., M, S.G., Lai, W., D, P.B., Patil, S.N. and L, H.K., 2022. ObjectDetect: A RealTime Object Detection Framework for Advanced Driver Assistant Systems Using YOLOv5. Wireless Communications and Mobile Computing, [online] 2022, p.9444360. https://doi.org/10.1155/2022/9444360.
Chen, Y.L., Wu, B.F., Huang, H.Y. and Fan, C.J., 2011. A RealTime Vision System for Nighttime Vehicle Detection and Traffic Surveillance. IEEE Transactions on Industrial Electronics, 58(5), pp.2030–2044. https://doi.org/10.1109/TIE.2010.2055771.
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.
Pangestu, I., 2022. Mengenal Pengertian CCTV: Fungsi, Jenis dan Cara Kerjanya, Jasa Pembuatan Website - Metafora Indonesia Tehnology. [online] idmetafora.com. Available at: <https://idmetafora.com/news/read/1411/Mengenal-Pengertian-CCTV-Fungsi-Jenis-dan-Cara-Kerjanya.html>.
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.
Wang, J., Yang, P., Liu, Y., Shang, D., Hui, X., Song, J. and Chen, X., 2023. Research on Improved YOLOv5 for LowLight Environment Object Detection. Electronics, 12(14). https://doi.org/10.3390/electronics12143089.
Wu, S., Ge, F. and Zhang, Y.,. A Vehicle LinePressing Detection Approach Based on YOLOv5 and DeepSort. In: 2022 IEEE 22nd International Conference on Communication Technology (ICCT). pp.1745–1749. https://doi.org/10.1109/ICCT56141.2022.10072680.
Yang, Y., Cheng, Z., Yu, H., Zhang, Y., Cheng, X., Zhang, Z. and Xie, G., 2022. MSENet: generative image inpainting with multiscale encoder. The Visual Computer, [online] 38(8), pp.2647–2659. https://doi.org/10.1007/s00371021021430.
Yoon, S. and Cho, J., 2023. LowLight Image Contrast Enhancement with Adaptive Noise Attenuator for Augmented Vehicle Detection. Electronics, 12(16). https://doi.org/10.3390/electronics12163517.
Bazzi, A., Murthy, J.S., M, S.G., Lai, W., D, P.B., Patil, S.N. and L, H.K., 2022. ObjectDetect: A RealTime Object Detection Framework for Advanced Driver Assistant Systems Using YOLOv5. Wireless Communications and Mobile Computing, [online] 2022, p.9444360. https://doi.org/10.1155/2022/9444360.
Chen, Y.L., Wu, B.F., Huang, H.Y. and Fan, C.J., 2011. A RealTime Vision System for Nighttime Vehicle Detection and Traffic Surveillance. IEEE Transactions on Industrial Electronics, 58(5), pp.2030–2044. https://doi.org/10.1109/TIE.2010.2055771.
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.
Pangestu, I., 2022. Mengenal Pengertian CCTV: Fungsi, Jenis dan Cara Kerjanya, Jasa Pembuatan Website - Metafora Indonesia Tehnology. [online] idmetafora.com. Available at: <https://idmetafora.com/news/read/1411/Mengenal-Pengertian-CCTV-Fungsi-Jenis-dan-Cara-Kerjanya.html>.
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.
Wang, J., Yang, P., Liu, Y., Shang, D., Hui, X., Song, J. and Chen, X., 2023. Research on Improved YOLOv5 for LowLight Environment Object Detection. Electronics, 12(14). https://doi.org/10.3390/electronics12143089.
Wu, S., Ge, F. and Zhang, Y.,. A Vehicle LinePressing Detection Approach Based on YOLOv5 and DeepSort. In: 2022 IEEE 22nd International Conference on Communication Technology (ICCT). pp.1745–1749. https://doi.org/10.1109/ICCT56141.2022.10072680.
Yang, Y., Cheng, Z., Yu, H., Zhang, Y., Cheng, X., Zhang, Z. and Xie, G., 2022. MSENet: generative image inpainting with multiscale encoder. The Visual Computer, [online] 38(8), pp.2647–2659. https://doi.org/10.1007/s00371021021430.
Yoon, S. and Cho, J., 2023. LowLight Image Contrast Enhancement with Adaptive Noise Attenuator for Augmented Vehicle Detection. Electronics, 12(16). https://doi.org/10.3390/electronics12163517.
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