{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:37:35Z","timestamp":1773790655116,"version":"3.50.1"},"reference-count":23,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>To solve the problem of poor object detection effect caused by uneven light and high noise in underground mines, this study proposes a TTFNet (training-time-friendly network)-based object detection algorithm for underground mines. First, CenterNet and TTFNet algorithms are introduced, then pooling is introduced into CSPNet basic structure to design a lightweight feature extraction network, at the same time optimizing the feature fusion way in the original algorithm, optimizing residual shrinkage network structure, and introducing it into object detection task. Experiments were conducted on the established underground data set. The results show that compared with the original algorithm, our proposed algorithm can still maintain similar accuracy while significantly reducing model parameters; compared with other anchor-based detection algorithms, it has achieved similar overall performance.<\/jats:p>","DOI":"10.1515\/comp-2024-0015","type":"journal-article","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T11:11:34Z","timestamp":1732619494000},"source":"Crossref","is-referenced-by-count":2,"title":["Mine underground object detection algorithm based on TTFNet and anchor-free"],"prefix":"10.1515","volume":"14","author":[{"given":"Zhen","family":"Song","sequence":"first","affiliation":[{"name":"School of Mechatronic Engineering, Southwest Petroleum University , Chengdu 610500 , China"},{"name":"Sichuan Province Science and Technology Resource Sharing Service Platform for Petroleum and Natural Gas Equipment Technology, Southwest Petroleum University , Chengdu 610500 , China"}]},{"given":"Xuwen","family":"Qing","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, Southwest Petroleum University , Chengdu 610500 , China"}]},{"given":"Meng","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Science, Southwest Petroleum University , Chengdu 610500 , China"}]},{"given":"Yuting","family":"Men","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, Southwest Petroleum University , Chengdu 610500 , China"}]}],"member":"374","published-online":{"date-parts":[[2024,11,26]]},"reference":[{"key":"2024112611112648922_j_comp-2024-0015_ref_001","unstructured":"Q. Sun, Research on the control of autonomous driving of intelligent scraper, M. S. Thesis, Department of Control Engineering, University of Jinan, Jinan, China, 2020."},{"key":"2024112611112648922_j_comp-2024-0015_ref_002","unstructured":"Y. Yao, \u201cApplication and development of large-scale and efficient intelligent mining technology and equipment in underground mines,\u201d Mining Equipment, vol. 4, pp. 17\u201320, 2018."},{"key":"2024112611112648922_j_comp-2024-0015_ref_003","unstructured":"D. Jiang and L. Wang, \u201cPresent situation and development trend of self-loading technology for underground load-haul-dump,\u201d Gold Science and Technology, vol. 29, no.1, pp. 35\u201342, 2021."},{"key":"2024112611112648922_j_comp-2024-0015_ref_004","unstructured":"J. Wu, Research on image enhancement and target tracking in underground mine, M.S. Thesis, Dept. College of Inf. and Comput., Taiyuan University of Technology, Taiyuan, China, 2021."},{"key":"2024112611112648922_j_comp-2024-0015_ref_005","doi-asserted-by":"crossref","unstructured":"S. Ren, K. He, R. Girshick, J. Sun, \u201cFaster R-CNN: Towards real-time object detection with region proposal networks,\u201d IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6. pp. 1137\u20131149, 2016.","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"2024112611112648922_j_comp-2024-0015_ref_006","doi-asserted-by":"crossref","unstructured":"W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.Y. Fu, et al., \u201cSSD: single shot multibox detector,\u201d In: Proceedings of the IEEE Conference on Computer Vision, 2016, pp. 21\u201337.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"2024112611112648922_j_comp-2024-0015_ref_007","doi-asserted-by":"crossref","unstructured":"T. Zhang, Z. Li, Z. Sun, and L. 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Chai, \u201cThe object detecting in dangerous areas for coal mine underground,\u201d Journal of China Coal Society, vol. 36, no. 3, pp. 527\u2013532, 2011."},{"key":"2024112611112648922_j_comp-2024-0015_ref_011","unstructured":"W. Li, C. Wei, and L. Wang, \u201cImproved Faster RCNN approach for pedestrian detection in underground coal mine,\u201d Computer Engineering and Applications Journal, vol. 55, no. 4, pp. 200\u2013207, 2019."},{"key":"2024112611112648922_j_comp-2024-0015_ref_012","unstructured":"T. Cui and L. Wang, \u201cResearch on application of YOLOV4 object detection algorithm in monitoring on masks wearing of coal miners,\u201d Journal of Safety Science and Technology, vol. 17, no. 10, pp. 66\u201371, 2021."},{"key":"2024112611112648922_j_comp-2024-0015_ref_013","unstructured":"B. Alexey, C. Wang, H. Liao, and H. Mark, Yolov4: Optimal speed and accuracy of object detection, 2020. [Online]. https:\/\/arxiv.org\/pdf\/2004.10934.pdf."},{"key":"2024112611112648922_j_comp-2024-0015_ref_014","unstructured":"F. Zhang, J. Luan, D. Cui, and Z. Xu, \u201cSSD-LeNet based method of mine moving target detection and recognition,\u201d Journal of Mining Science and Technology, vol. 6, no. 1, pp. 100\u2013108, 2021."},{"key":"2024112611112648922_j_comp-2024-0015_ref_015","unstructured":"S. Hao, X. Zhang, X. Ma, S. Sun, and H. Wen, \u201cForeign object detection in coal mine conveyor belt based on CBAM-YOLOv5,\u201d Journal of China Coal Society, vol. 47, no. 11, pp. 4147\u20134156, 2022."},{"key":"2024112611112648922_j_comp-2024-0015_ref_016","unstructured":"D. Zhang and Y. Jiang, \u201cLightweight target detection method of drilling rig based on attention mechanism and inverse residual structure,\u201d Journal of Electronic Measurement and Instrument, vol. 36, no. 11, pp. 201\u2013210, 2022."},{"key":"2024112611112648922_j_comp-2024-0015_ref_017","unstructured":"C. Du, \u201cAnti-collision system of mining and transportation equipment in coal mine based on multi-technology integration,\u201d Journal of China Coal Society, vol. 45, no. S2, pp. 1060\u20131068, 2020."},{"key":"2024112611112648922_j_comp-2024-0015_ref_018","doi-asserted-by":"crossref","unstructured":"Z. Liu, T. Zeng, G. Xu, Z. Yang, H. Liu, and D. Cai, \u201cTraining-time-friendly network for real-time object detection,\u201d Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 07, pp. 11685\u201311692, 2020.","DOI":"10.1609\/aaai.v34i07.6838"},{"key":"2024112611112648922_j_comp-2024-0015_ref_019","unstructured":"X. Zhou, D. Wang, and P. Kr\u00e4henb\u00fchl, Objects as points, 2019, [Online]. https:\/\/arxiv.org\/pdf\/1904.07850.pdf."},{"key":"2024112611112648922_j_comp-2024-0015_ref_020","unstructured":"Z. Luo, Object detection algorithm in complex background based on machine vision, M.S. Thesis, Department of Mechanical Engineering, University of Electronic Science and Technology of China, Chengdu, China, 2021."},{"key":"2024112611112648922_j_comp-2024-0015_ref_021","doi-asserted-by":"crossref","unstructured":"C. Y. Wang, H. Y. Mark Liao, Y. H. Wu, P. Y. Chen, J. W. Hsieh, and I. H. Yeh, CSPNet: A New Backbone that can Enhance Learning Capability of CNN, CVPRW, Seattle, WA, USA, 2020, pp. 1571\u20131580, 10.1109\/CVPRW50498.2020.00203.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"2024112611112648922_j_comp-2024-0015_ref_022","doi-asserted-by":"crossref","unstructured":"M. Zhao, S. Zhong, X. Fu, B. Tang, and M. Pecht, \u201cDeep residual shrinkage networks for fault diagnosis,\u201d IEEE Transactions on Industrial Informatics, vol. 16, no. 7, pp. 4681\u20134690, 2019.","DOI":"10.1109\/TII.2019.2943898"},{"key":"2024112611112648922_j_comp-2024-0015_ref_023","doi-asserted-by":"crossref","unstructured":"Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, and Q. 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