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Zhang, Y. Wang, F. Dayoub, and N. Sunderhauf, \u201cVarifocalnet: An iou-aware dense object detector,\u201d Proc. IEEE\/CVF conference on computer vision and pattern recognition, pp.8514-8523, 2021.","DOI":"10.1109\/CVPR46437.2021.00841"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] S. Zhang, Y. Wu, C. Men, and X. Li, \u201cTiny YOLO optimization oriented bus passenger object detection,\u201d Chinese Journal of Electronics, vol.29, no.1, pp.132-138, Jan. 2020. 10.1049\/cje.2019.11.002","DOI":"10.1049\/cje.2019.11.002"},{"key":"3","unstructured":"[3] A.G. Howard, \u201cMobilenets: Efficient convolutional neural networks for mobile vision applications,\u201d arXiv preprint arXiv:1704.04861, 2017."},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] T.Y. Lin, P. Doll\u00e1r, R. Girshick, K. He, B. Hariharan, and S. Belongie, \u201cFeature pyramid networks for object detection,\u201d Proc. IEEE conference on computer vision and pattern recognition, pp.2117-2125, 2017.","DOI":"10.1109\/CVPR.2017.106"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] J. Chen, H. Mai, L. Luo, X. Chen, and K. Wu, \u201cEffective feature fusion network in BIFPN for small object detection,\u201d 2021 IEEE international conference on image processing (ICIP), pp.699-703, IEEE, Sept. 2021. 10.1109\/icip42928.2021.9506347","DOI":"10.1109\/ICIP42928.2021.9506347"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[15] A. Wang, T. Peng, H. Cao, Y. Xu, X. Wei, and B. Cui, \u201cTIA-YOLOv5: An improved YOLOv5 network for real-time detection of crop and weed in the field,\u201d Frontiers in Plant Science, vol.13, 1091655, Dec. 2022. 10.3389\/fpls.2022.1091655","DOI":"10.3389\/fpls.2022.1091655"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[9] K. Duan, S. Bai, L. Xie, H. Qi, Q. Huang, and Q. Tian, \u201cCenternet: Keypoint triplets for object detection,\u201d Proc. IEEE\/CVF international conference on computer vision, pp.6569-6578, 2019. 10.1109\/iccv.2019.00667","DOI":"10.1109\/ICCV.2019.00667"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[10] C. Chen, Y. Zhang, Q. Lv, S. Wei, X. Wang, X. Sun, and J. Dong, \u201cRrnet: A hybrid detector for object detection in drone-captured images,\u201d Proc. IEEE\/CVF international conference on computer vision workshops, 2019. 10.1109\/iccvw.2019.00018","DOI":"10.1109\/ICCVW.2019.00018"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[11] J. Shang, J. Wang, S. Liu, C. Wang, and B. Zheng, \u201cSmall target detection algorithm for UAV aerial photography based on improved YOLOv5s,\u201d Electronics, vol.12, no.11, 2434, May 2023. 10.3390\/electronics12112434","DOI":"10.3390\/electronics12112434"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[12] H. Liu, F. Sun, J. Gu, and L. Deng, \u201cSf-yolov5: A lightweight small object detection algorithm based on improved feature fusion mode,\u201d Sensors, vol.22, no.15, 5817, Aug. 2022. 10.3390\/s22155817","DOI":"10.3390\/s22155817"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[13] H. Liu, X. Duan, H. Lou, J. Gu, H. Chen, and L. Bi, \u201cImproved GBS-YOLOv5 algorithm based on YOLOv5 applied to UAV intelligent traffic,\u201d Scientific Reports, vol.13, no.1, 9577, Jun. 2023. 10.1038\/s41598-023-36781-2","DOI":"10.1038\/s41598-023-36781-2"},{"key":"12","unstructured":"[14] A. Dosovitskiy, \u201cAn image is worth 16x16 words: Transformers for image recognition at scale,\u201d arXiv preprint arXiv:2010.11929, 2020."},{"key":"13","doi-asserted-by":"publisher","unstructured":"[15] A. Wang, T. Peng, H. Cao, Y. Xu, X. Wei, and B. 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Cao, \u201cLearning to upsample by learning to sample,\u201d Proc. IEEE\/CVF International Conference on Computer Vision, pp.6004-6014, 2023. 10.1109\/iccv51070.2023.00554","DOI":"10.1109\/ICCV51070.2023.00554"},{"key":"17","unstructured":"[19] X. Chen, X. Wang, W. Zhang, X. Kong, Y. Qiao, J. Zhou, and C. Dong, \u201cHat: Hybrid attention transformer for image restoration,\u201d arXiv preprint arXiv:2309.05239, Sept. 2023."},{"key":"18","unstructured":"[20] A. Wang, H. Chen, L. Liu, K. Chen, Z. Lin, J. Han, and G. Ding, \u201cYolov10: Real-time end-to-end object detection,\u201d arXiv preprint arXiv:2405.14458, May 2024."},{"key":"19","unstructured":"[21] D. Du, P. Zhu, L. Wen, X. Bian, H. Lin, Q. Hu, T. Peng, J. Zheng, X. Wang, Y. Zhang, and L. Bo, \u201cVisDrone-DET2019: The vision meets drone object detection in image challenge results,\u201d Proc. IEEE\/CVF international conference on computer vision workshops, 2019."},{"key":"20","unstructured":"[22] C. Berner, G. Brockman, B. Chan, V. Cheung, P. D\u0119biak, C. Dennison, D. Farhi, Q. Fischer, S. Hashme, C. Hesse, and R. J\u00f3zefowicz, \u201cDota 2 with large scale deep reinforcement learning,\u201d arXiv preprint arXiv:1912.06680, Dec. 2019."},{"key":"21","doi-asserted-by":"crossref","unstructured":"[23] G. Wang, Y. Chen, P. An, H. Hong, J. Hu, and T. Huang, \u201cUAV-YOLOv8: A small-object-detection model based on improved YOLOv8 for UAV aerial photography scenarios,\u201d Sensors, vol.23, no.16, 7190, Aug. 2023. 10.3390\/s23167190","DOI":"10.3390\/s23167190"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[24] Y. Li, Q. Fan, H. Huang, Z. Han, and Q. Gu, \u201cA modified YOLOv8 detection network for UAV aerial image recognition,\u201d Drones, vol.7, no.5, 304, May 2023. 10.3390\/drones7050304","DOI":"10.3390\/drones7050304"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[25] T. Wu and Y. 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Gu, \u201cTriChronoNet: Advancing electricity price prediction with Multi-module fusion,\u201d Applied Energy, vol.371, 123626, Oct. 2024. 10.1016\/j.apenergy.2024.123626","DOI":"10.1016\/j.apenergy.2024.123626"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[29] Y. Lu, W. Wang, R. Bai, S. Zhou, L. Garg, A.K. Bashir, W. Jiang, and X. Hu, \u201cHyper-relational interaction modeling in multi-modal trajectory prediction for intelligent connected vehicles in smart cites,\u201d Information Fusion, vol.114, 102682, Feb. 2025. 10.1016\/j.inffus.2024.102682","DOI":"10.1016\/j.inffus.2024.102682"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[30] B. Yang, X. Wang, Y. Xing, C. Cheng, W. Jiang, and Q. 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