Perbandingan Kinerja Model YOLOv6 dan YOLOv7 dalam Mendeteksi Sampah Perairan
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Naskah ini akan diterbitkan di Jurnal Internasional INASS
Referensi
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. ArXiv. https://doi.org/10.48550/arXiv.2004.10934
Chandra, V., Sarkar, P. G., & Singh, V. (2020). Mitral Valve Leaflet Tracking in Echocardiography using Custom Yolo3. Procedia Computer Science, 171, 820–828. https://doi.org/10.1016/j.procs.2020.04.089
Cheng, Y., Zhu, J., Jiang, M., Fu, J., Pang, C., Wang, P., Sankaran, K., Onabola, O., Liu, Y., Liu, D., & Bengio, Y. (2021). FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 10953–10962. https://github.com/ORCA-Uboat/FloW-Dataset.
Ge, Z., Liu, S., Wang, F., Li, Z., & Sun, J. (2021). YOLOX: Exceeding YOLO Series in 2021. ArXiv, 5, 12. https://github.com/ultralytics/yolov3 Jiang, P., Ergu, D., Liu, F., Cai, Y., & Ma, B. (2021). A Review of Yolo Algorithm Developments. Procedia Computer Science, 199, 1066–1073. https://doi.org/10.1016/j.procs.2022.01.135
Jocher, G., Chaurasia, A., & Qiu, J. (2023). Ultralytics YOLOv8. https://github.com/ultralytics/ultralytics
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., V, A., Montes, D., Wang, Z., Fati, C., Nadar, J., Laughing, … Jain, M. (2022). ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation (7). Zenodo. https://doi.org/10.5281/zenodo.7347926
Kementerian Kelautan dan Perikanan. (2023). Sampah Laut (Marine Debris). Direktorat Pendayagunaan Pesisir Dan Pulau-Pulau Kecil (P4K). https://kkp.go.id/djprl/p4k/page/1994-sampah-laut-marine-debris
Kementerian Lingkungan Hidup dan Kehutanan. (2022). CAPAIAN KINERJA PENGELOLAAN SAMPAH. Sistem Informasi Pengelolaan Sampah Nasional (SIPSN) – Kementerian Lingkungan Hidup Dan Kehutanan. https://sipsn.menlhk.go.id/sipsn/
Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., & Chu, X. (2023). YOLOv6 v3.0: A Full-Scale Reloading. ArXiv. http://arxiv.org/abs/2301.05586
Redmon, J., & Farhadi, A. (2016). YOLO9000: Better, Faster, Stronger. ArXiv. https://doi.org/10.48550/arXiv.1612.08242
Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. ArXiv. https://pjreddie.com/yolo/.
Tharani, M., Amin, A. W., Maaz, M., & Taj, M. (2020). Attention Neural Network for Trash Detection on Water Channels. ArXiv. http://arxiv.org/abs/2007.04639
Tian, D., Lin, C., Zhou, J., Duan, X., Cao, Y., Zhao, D., & Cao, D. (2022). SA-YOLOv3: An Efficient and Accurate Object Detector Using Self Attention Mechanism for Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems, 23(5), 4099–4110. https://doi.org/10.1109/TITS.2020.3041278
Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. ArXiv. https://doi.org/10.48550/arXiv.2207.02696
Widjaja, G., & Lovianda Gunawan, S. (2022). DAMPAK SAMPAH LIMBAH RUMAH TANGGA TERHADAP KESEHATAN LINGKUNGAN. ZAHRA: JOURNAL OF HEALTH AND MEDICAL RESEARCH, 2(Oktober), 266–275.
Xu, S., Wang, X., Lv, W., Chang, Q., Cui, C., Deng, K., Wang, G., Dang, Q., Wei, S., Du, Y., & Lai, B. (2022). PP-YOLOE: An evolved version of YOLO. ArXiv. https://doi.org/10.48550/arXiv.2203.16250
Zhang, Z., Lu, X., Cao, G., Yang, Y., Jiao, L., & Liu, F. (2021). ViT-YOLO:Transformer-Based YOLO for Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2799–2808.
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. ArXiv. https://doi.org/10.48550/arXiv.2004.10934
Chandra, V., Sarkar, P. G., & Singh, V. (2020). Mitral Valve Leaflet Tracking in Echocardiography using Custom Yolo3. Procedia Computer Science, 171, 820–828. https://doi.org/10.1016/j.procs.2020.04.089
Cheng, Y., Zhu, J., Jiang, M., Fu, J., Pang, C., Wang, P., Sankaran, K., Onabola, O., Liu, Y., Liu, D., & Bengio, Y. (2021). FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 10953–10962. https://github.com/ORCA-Uboat/FloW-Dataset.
Ge, Z., Liu, S., Wang, F., Li, Z., & Sun, J. (2021). YOLOX: Exceeding YOLO Series in 2021. ArXiv, 5, 12. https://github.com/ultralytics/yolov3 Jiang, P., Ergu, D., Liu, F., Cai, Y., & Ma, B. (2021). A Review of Yolo Algorithm Developments. Procedia Computer Science, 199, 1066–1073. https://doi.org/10.1016/j.procs.2022.01.135
Jocher, G., Chaurasia, A., & Qiu, J. (2023). Ultralytics YOLOv8. https://github.com/ultralytics/ultralytics
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., V, A., Montes, D., Wang, Z., Fati, C., Nadar, J., Laughing, … Jain, M. (2022). ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation (7). Zenodo. https://doi.org/10.5281/zenodo.7347926
Kementerian Kelautan dan Perikanan. (2023). Sampah Laut (Marine Debris). Direktorat Pendayagunaan Pesisir Dan Pulau-Pulau Kecil (P4K). https://kkp.go.id/djprl/p4k/page/1994-sampah-laut-marine-debris
Kementerian Lingkungan Hidup dan Kehutanan. (2022). CAPAIAN KINERJA PENGELOLAAN SAMPAH. Sistem Informasi Pengelolaan Sampah Nasional (SIPSN) – Kementerian Lingkungan Hidup Dan Kehutanan. https://sipsn.menlhk.go.id/sipsn/
Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., & Chu, X. (2023). YOLOv6 v3.0: A Full-Scale Reloading. ArXiv. http://arxiv.org/abs/2301.05586
Redmon, J., & Farhadi, A. (2016). YOLO9000: Better, Faster, Stronger. ArXiv. https://doi.org/10.48550/arXiv.1612.08242
Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. ArXiv. https://pjreddie.com/yolo/.
Tharani, M., Amin, A. W., Maaz, M., & Taj, M. (2020). Attention Neural Network for Trash Detection on Water Channels. ArXiv. http://arxiv.org/abs/2007.04639
Tian, D., Lin, C., Zhou, J., Duan, X., Cao, Y., Zhao, D., & Cao, D. (2022). SA-YOLOv3: An Efficient and Accurate Object Detector Using Self Attention Mechanism for Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems, 23(5), 4099–4110. https://doi.org/10.1109/TITS.2020.3041278
Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. ArXiv. https://doi.org/10.48550/arXiv.2207.02696
Widjaja, G., & Lovianda Gunawan, S. (2022). DAMPAK SAMPAH LIMBAH RUMAH TANGGA TERHADAP KESEHATAN LINGKUNGAN. ZAHRA: JOURNAL OF HEALTH AND MEDICAL RESEARCH, 2(Oktober), 266–275.
Xu, S., Wang, X., Lv, W., Chang, Q., Cui, C., Deng, K., Wang, G., Dang, Q., Wei, S., Du, Y., & Lai, B. (2022). PP-YOLOE: An evolved version of YOLO. ArXiv. https://doi.org/10.48550/arXiv.2203.16250
Zhang, Z., Lu, X., Cao, G., Yang, Y., Jiao, L., & Liu, F. (2021). ViT-YOLO:Transformer-Based YOLO for Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2799–2808.
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