[1] ROCCIA F, SOBRERO F, RAVEGGI E et al. European multicenter prospective analysis of the use of maxillomandibular fixation for mandibular fractures treated with open reduction and internal fixation[J]. Journal of Stomatology， Oral and Maxillofacial Surgery, 124, 101376(2023).

[2] ZHOU T, HOU S B, LU H L et al. Exploring the improved mechanism of U-Net and its application in medical image segmentation[J]. Journal of Biomedical Engineering, 39, 806-825(2022).

[3] MENG X H, WU D J, WANG Z et al. A fully automated rib fracture detection system on chest CT images and its impact on radiologist performance[J]. Skeletal Radiology, 50, 1821-1828(2021).

[4] ZHOU Q Q, TANG W, WANG J S et al. Automatic detection and classification of rib fractures based on patients' CT images and clinical information via convolutional neural network[J]. European Radiology, 31, 3815-3825(2021).

[5] XUE L Y, YAN W N, LUO P et al. Detection and localization of hand fractures based on GA_Faster R-CNN[J]. Alexandria Engineering Journal, 60, 4555-4562(2021).

[6] KITAMURA G, CHUNG C Y, MOORE B E. Ankle fracture detection utilizing a convolutional neural network ensemble implemented with a small sample， de novo training， and multiview incorporation[J]. Journal of Digital Imaging, 32, 672-677(2019).

[7] YUAN G, LIU H Z, JIANG L et al. CCE-Net： a rib fracture diagnosis network based on contralateral， contextual， and edge enhanced modules[J]. Biomedical Signal Processing and Control, 75, 103620(2022).

[8] LIU P R, LU L, CHEN Y F et al. Artificial intelligence to detect the femoral intertrochanteric fracture： the arrival of the intelligent-medicine era[J]. Frontiers in Bioengineering and Biotechnology, 10, 927926(2022).

[9] WU J F, LIU N J, LI X J et al. Convolutional neural network for detecting rib fractures on chest radiographs： a feasibility study[J]. BMC Medical Imaging, 23, 1-12(2023).

[10] WARIN K, LIMPRASERT W, SUEBNUKARN S et al. Assessment of deep convolutional neural network models for mandibular fracture detection in panoramic radiographs[J]. International Journal of Oral and Maxillofacial Surgery, 51, 1488-1494(2022).

[11] WANG X B, XU Z N, TONG Y H et al. Detection and classification of mandibular fracture on CT scan using deep convolutional neural network[J]. Clinical Oral Investigations, 26, 4593-4601(2022).

[12] VINAYAHALINGAM S, VAN NISTELROOIJ N, VAN GINNEKEN B et al. Detection of mandibular fractures on panoramic radiographs using deep learning[J]. Scientific Reports, 12, 19596(2022).

[13] SON D M, YOON Y A, KWON H J et al. Automatic detection of mandibular fractures in panoramic radiographs using deep learning[J]. Diagnostics, 11, 933(2021).

[14] ZHOU T, YE X Y, LU H L et al. Dense convolutional network and its application in medical image analysis[J]. BioMed Research International, 2022, 23848301-22(2022).

[15] MEHTA S, RASTEGARI M. MobileViT： light-weight， general-purpose， and mobile-friendly vision transformer[C](2021).

[16] ZHANG S, CHE S B, LIU Z et al. A real-time and lightweight traffic sign detection method based on ghost-YOLO[J]. Multimedia Tools and Applications, 82, 26063-26087(2023).

[17] WANG S, QU Z, LI C et al. BANet： Small and multi-object detection with a bidirectional attention network for traffic scenes[J]. Engineering Applications of Artificial Intelligence, 117, 105504(2023).

[18] CHEN Y T, XIA R L, YANG K et al. MFFN： image super-resolution via multi-level features fusion network[J]. The Visual Computer, 1-16(2023).

[19] JOSEPH R. YOLOv3：An incremental improvement[C](2018).

[20] ALEXEY B, WANG C Y, LIAO H Y M et al. YOLOv4：Optimal speed and accuracy of object detection[J]. Image and Video Processing(2020).

[21] REN S Q, HE K M, GIRSHICK R et al. Faster R-CNN： towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

[22] ZHOU X Y, WANG D Q, KRÄHENBÜHL P et al. Objects as Points[C], 1904(2019).

[23] GE Z, LIU S, WANG F et al. YOLOX： Exceeding YOLO Series in 2021[webpage]. arXiv, 2107-08430(2021). https：//arxiv.org/abs/2107.08430.pdf

[24] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7： Trainable Bag-of-Freebies Sets New State-of-the-art for Real-time Object Detectors[webpage]. arXiv, 2207-02696(2022). https：//arxiv.org/abs/2207.02696.pdf

[25] ZHOU T, CHANG X Y, LU H L et al. Pooling operations in deep learning： from “invariable” to “variable”[J]. BioMed Research International, 2022, 1-17(2022).