[1] Che, Zhaoxue, and Hong Yang. "Application of open-pit and underground mining technology for residual coal of end slopes." Mining Science and Technology (China) 20, no. 2 (2010): 266–270.
[2] Bernardi, Lou, Mustafa Kumral, and Matt Renaud. "Comparison of fixed and mobile in-pit crushing and conveying and truck-shovel systems used in mineral industries through discrete-event simulation." Simulation Modelling Practice and Theory 103 (2020): 102100.
[3] Ng, Felix, Jennifer A. Harding, and Jacqueline Glass. "Improving hydraulic excavator performance through in line hydraulic oil contamination monitoring." Mechanical Systems and Signal Processing 83 (2017): 176–193.
[4] Deng, Zhigang, Longjiang Wang, Weijian Liu, Zhenwei Wang, and E. Qiule. "Mathematical modeling and fuzzy approach for disaster analysis on geo-spatial rock mass in open-pit mining." Computer Communications 150 (2020): 384–392.
[5] Luo, Shuo, Haifeng Li, and Hong Shen. "Deeply supervised convolutional neural network for shadow detection based on a novel aerial shadow imagery dataset." ISPRS Journal of Photogrammetry and Remote Sensing 167 (2020): 443–457.
[6] Luo, Xiling, and Hong Zhang. "Missing tooth detection with laser range sensing." In Proceedings of the Fifth World Congress on Intelligent Control and Automation, vol. 4, pp. 3607–3610, 2004.
[7] He, Li, Hua Wang, and Hong Zhang. "Object detection by parts using appearance, structural and shape features." In 2011 IEEE International Conference on Mechatronics and Automation, pp. 489–494, 2011.
[8] Duan, Yuxin, Weihao Du, and Zongqiang Zeng. "Detection method for missing teeth of electric shovel based on machine vision." Industrial Mine Automation 44, no. 7 (2018): 75–79.
[9] Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: towards real-time object detection with region proposal networks." IEEE Transactions on Pattern Analysis and Machine Intelligence 39, no. 6 (2016): 1137–1149.
[10] Zou, Zhengxia, Keyan Chen, Zhenwei Shi, Yuhong Guo, and Jieping Ye. "Object detection in 20 years: a survey." Proceedings of the IEEE 111, no. 3 (2023): 257–276.
[11] Shariati, Hooman, Anuar Yeraliyev, Bahman Terai, Sadegh Tafazoli, and Mohsen Ramezani. "Towards autonomous mining via intelligent excavators." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 26–32, 2019.
[12] Ji, Shengfei, Wei Li, Bo Zhang, Lingwei Zhou, and Chenxi Duan. "Bucket teeth detection based on faster region convolutional neural network." IEEE Access 9 (2021): 17649–17661.
[13] Liu, Xiaobo, Xianglong Qi, and Yiming Jiang. "Electric shovel teeth missing detection method based on deep learning." Computational Intelligence and Neuroscience (2021): 6503029.
[14] Liu, Wei, Dumitru Anguelov, Dragos Erhan, Christian Szegedy, Scott Reed, Chieh-Yuan Fu, and Alexander C. Berg. "SSD: single shot multibox detector." In Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, Proceedings, Part I, Springer, pp. 21–37, 2016.
[15] Redmon, Joseph, Santosh Divvala, Ross Girshick, and Ali Farhadi. "You only look once: unified, real-time object detection." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788, 2016.
[16] Lin, Tsung-Yi, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. "Focal loss for dense object detection." In Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988, 2017.
[17] Duan, Kaiwen, Song Bai, Lingxi Xie, Honggang Qi, Qingming Huang, and Qi Tian. "CenterNet: keypoint triplets for object detection." In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6569–6578, 2019.
[18] Redmon, Joseph, and Ali Farhadi. "YOLO9000: better, faster, stronger." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271, 2017.
[19] Farhadi, Ali, and Joseph Redmon. "YOLOv3: an incremental improvement." arXiv preprint arXiv:1804.02767 (2018).
[20] Bochkovskiy, Alexey, Chien-Yao Wang, and Hong-Yuan Mark Liao. "YOLOv4: optimal speed and accuracy of object detection." arXiv preprint arXiv:2004.10934 (2020).
[21] Jocher, Glenn, Ayush Chaurasia, Alex Stoken, Jirka Borovec, Yonghyeok Kwon, Kelvin Michael, Jonathan Fang, Daniel Montes, Josef Nader, and Pawel Skalski. "Ultralytics/YOLOv5: v6.1 – TensorRT, TensorFlow Edge TPU and OpenVINO export and inference." Zenodo, 2022.
[22] Paszke, Adam, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, and Luca Antiga. "PyTorch: an imperative style, high-performance deep learning library." In Advances in Neural Information Processing Systems, vol. 32, 2019.
[23] Elfwing, Stefan, Eiji Uchibe, and Kenji Doya. "Sigmoid-weighted linear units for neural network function approximation in reinforcement learning." Neural Networks 107 (2018): 3–11.
[24] Li, Chuyi, Lulu Li, Hongliang Jiang, Kaiheng Weng, Yifei Geng, Liang Li, Zaidan Ke, Qingyuan Li, Meng Cheng, and Weiqiang Nie. "YOLOv6: a single-stage object detection framework for industrial applications." arXiv preprint arXiv:2209.02976 (2022).
[25] Wang, Chien-Yao, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. "YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464–7475, 2023.
[26] Jocher, Glenn, Ayush Chaurasia, and Joseph Qiu. "Ultralytics YOLOv8 (version 8.0.0)." Zenodo, 2023.
[27] Zhang, Shihao, Hekai Yang, Chunhua Yang, Wenxia Yuan, Xinghui Li, Xinghua Wang, Yinsong Zhang, Xiaobo Cai, Yubo Sheng, and Xiujuan Deng. "Edge device detection of tea leaves with one bud and two leaves based on ShuffleNetv2-YOLOv5-Lite-E." Agronomy 13, no. 2 (2023): 577.
[28] Xing, Zhaoxing, Zhongbing Zhang, Xiaoyue Yao, Yulong Qin, and Lei Jia. "Rail wheel tread defect detection using improved YOLOv3." Measurement 203 (2022): 111959.
[29] Cui, Ming, Yong Lou, Yanhong Ge, and Kai Wang. "LES-YOLO: a lightweight pinecone detection algorithm based on improved YOLOv4-tiny network." Computers and Electronics in Agriculture 205 (2023): 107613.
[30] Hu, Wei, Jie Xiong, Jun Liang, Zhen Xie, Ze Liu, Qiang Huang, and Zheng Yang. "A method of citrus epidermis defects detection based on an improved YOLOv5." Biosystems Engineering 227 (2023): 19–35.
[31] Hu, Jie, Li Shen, and Gang Sun. "Squeeze-and-excitation networks." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141, 2018.
[32] Petersen, Steven E., and Michael I. Posner. "The attention system of the human brain: 20 years after." Annual Review of Neuroscience 35 (2012): 73–89.
[33] Han, Kai, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, and Chang Xu. "GhostNet: more features from cheap operations." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1580–1589, 2020.
[34] Chollet, Francois. "Xception: deep learning with depthwise separable convolutions." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258, 2017.
[35] Wang, Jielan, Hongguang Xiao, Lifu Chen, Jin Xing, Zhouhao Pan, Ru Luo, and Xingmin Cai. "Integrating weighted feature fusion and the spatial attention module with convolutional neural networks for automatic aircraft detection from SAR images." Remote Sensing 13, no. 5 (2021): 910.
[36] He, Jiabo, Sarah Erfani, Xingjun Ma, James Bailey, Ying Chi, and Xian-Sheng Hua. "$\alpha$-IoU: a family of power intersection over union losses for bounding box regression." In Advances in Neural Information Processing Systems 34 (2021): 20230–20242.
[37] Box, George E. P., and David R. Cox. "An analysis of transformations." Journal of the Royal Statistical Society. Series B, Statistical Methodology 26, no. 2 (1964): 211–243.
[38] Powers, David M. W. "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation." arXiv preprint arXiv:2010.16061 (2020).
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