{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T03:21:45Z","timestamp":1775186505780,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T00:00:00Z","timestamp":1685232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["NSFC51274150"],"award-info":[{"award-number":["NSFC51274150"]}]},{"name":"National Natural Science Foundation of China","award":["18YFZCGX00930"],"award-info":[{"award-number":["18YFZCGX00930"]}]},{"name":"Key R&amp;D Programme Science and Technology Support Projects of Tianjin","award":["NSFC51274150"],"award-info":[{"award-number":["NSFC51274150"]}]},{"name":"Key R&amp;D Programme Science and Technology Support Projects of Tianjin","award":["18YFZCGX00930"],"award-info":[{"award-number":["18YFZCGX00930"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Coal production often involves a substantial presence of gangue and foreign matter, which not only impacts the thermal properties of coal and but also leads to damage to transportation equipment. Selection robots for gangue removal have garnered attention in research. However, existing methods suffer from limitations, including slow selection speed and low recognition accuracy. To address these issues, this study proposes an improved method for detecting gangue and foreign matter in coal, utilizing a gangue selection robot with an enhanced YOLOv7 network model. The proposed approach entails the collection of coal, gangue, and foreign matter images using an industrial camera, which are then utilized to create an image dataset. The method involves reducing the number of convolution layers of the backbone, adding a small size detection layer to the head to enhance the small target detection, introducing a contextual transformer networks (COTN) module, employing a distance intersection over union (DIoU) loss border regression loss function to calculate the overlap between predicted and real frames, and incorporating a dual path attention mechanism. These enhancements culminate in the development of a novel YOLOv71 + COTN network model. Subsequently, the YOLOv71 + COTN network model was trained and evaluated using the prepared dataset. Experimental results demonstrated the superior performance of the proposed method compared to the original YOLOv7 network model. Specifically, the method exhibits a 3.97% increase in precision, a 4.4% increase in recall, and a 4.5% increase in mAP0.5. Additionally, the method reduced GPU memory consumption during runtime, enabling fast and accurate detection of gangue and foreign matter.<\/jats:p>","DOI":"10.3390\/s23115140","type":"journal-article","created":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T15:29:52Z","timestamp":1685287792000},"page":"5140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Improved YOLOv7 Network Model for Gangue Selection Robot for Gangue and Foreign Matter Detection in Coal"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0004-8199","authenticated-orcid":false,"given":"Dengjie","family":"Yang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Tiangong University, Tianjin 300387, China"},{"name":"Tianjin Photoelectric Detection Technology and System Key Laboratory, Tiangong University, Tianjin 300387, China"}]},{"given":"Changyun","family":"Miao","sequence":"additional","affiliation":[{"name":"Tianjin Photoelectric Detection Technology and System Key Laboratory, Tiangong University, Tianjin 300387, China"},{"name":"School of Electronics and Information Engineering, Tiangong University, Tianjin 300387, China"}]},{"given":"Xianguo","family":"Li","sequence":"additional","affiliation":[{"name":"Tianjin Photoelectric Detection Technology and System Key Laboratory, Tiangong University, Tianjin 300387, China"},{"name":"School of Electronics and Information Engineering, Tiangong University, Tianjin 300387, China"}]},{"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Tiangong University, Tianjin 300387, China"},{"name":"Tianjin Photoelectric Detection Technology and System Key Laboratory, Tiangong University, Tianjin 300387, China"},{"name":"Center for Engineering Internship and Training, Tiangong University, Tianjin 300387, China"}]},{"given":"Yimin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Tiangong University, Tianjin 300387, China"},{"name":"Tianjin Photoelectric Detection Technology and System Key Laboratory, Tiangong University, Tianjin 300387, China"}]},{"given":"Yao","family":"Zheng","sequence":"additional","affiliation":[{"name":"Tianjin Photoelectric Detection Technology and System Key Laboratory, Tiangong University, Tianjin 300387, China"},{"name":"School of Electronics and Information Engineering, Tiangong University, Tianjin 300387, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, P., Tian, H., Cao, X., Qiao, X., Gong, L., Duan, X., Qiu, Y., and Su, Y. (2022). Pick\u2013and\u2013Place Trajectory Planning and Robust Adaptive Fuzzy Tracking Control for Cable\u2013Based Gangue\u2013Sorting Robots with Model Uncertainties and External Disturbances. Machines, 10.","DOI":"10.3390\/machines10080714"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, P., Ma, H., Zhang, Y., Cao, X., Wu, X., Wei, X., and Zhou, W. (2023). Trajectory Planning for Coal Gangue Sorting Robot Tracking Fast-Mass Target under Multiple Constraints. Sensors, 23.","DOI":"10.3390\/s23094412"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, P., Ma, H., Cao, X., Zhang, X., Duan, X., and Nie, Z. (2023). Minimum Dynamic Cable Tension Workspace Generation Techniques and Cable Tension Sensitivity Analysis Methods for Cable-Suspended Gangue-Sorting Robots. Machines, 11.","DOI":"10.3390\/machines11030338"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ma, H., Wei, X., Wang, P., Zhang, Y., Cao, X., and Zhou, W. (2022). Multi-Arm Global Cooperative Coal Gangue Sorting Method Based on Improved Hungarian Algorithm. Sensors, 22.","DOI":"10.3390\/s22207987"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"110530","DOI":"10.1016\/j.measurement.2021.110530","article-title":"Detection of coal and gangue based on improved YOLOv5. 1 which embedded scSE module","volume":"188","author":"Yan","year":"2022","journal-title":"Measurement"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gao, R., Sun, Z., Li, W., Pei, L., and Xiao, L. (2020). Automatic coal and gangue segmentation using u-net based fully convolutional networks. Energies, 13.","DOI":"10.3390\/en13040829"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"184686","DOI":"10.1109\/ACCESS.2019.2961075","article-title":"An image-based hierarchical deep learning framework for coal and gangue detection","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Loffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Loffe, S., Vanhoucke, V., and Alemi, A. (2016, January 4\u20139). Inception\u2014v4, inception\u2014Resnet and the impact of residual connections on learning. Proceedings of the National Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 11\u201314). Identity mappings in deep residual networks. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part IV 14.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wu, C., Zhang, Z., Zhu, Y., Lin, H., Zhang, Z., Sun, Y., He, T., Mueller, J., and Manmatha, R. (2022, January 19\u201320). Resnest: Split-attention networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00309"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2020.01.085","article-title":"Recent advances in deep learning for object detection","volume":"396","author":"Wu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE international conference on computer vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image Super-Resolution Using Deep Convolutional Networks","volume":"Volume 38","author":"Dong","year":"2016","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"Volume 39","author":"Ren","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., and Berg, A. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part I 14.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_21","unstructured":"Fu, C., Liu, W., Ranga, A., Tyagi, A., and Berg, A. (2017). Dssd: Deconvolutional single shot detector. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_24","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_25","unstructured":"Bochkovskiy, A., Wang, C., and Liao, H. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13\u201319). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_28","unstructured":"Woo, S., Park, J., Lee, J., and Kweon, I. (2018). Proceedings of the 15th European Conference on Computer Vision (ECCV), Munich, Germany, 8\u201314 September 2018, Springer."},{"key":"ref_29","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_30","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1080\/00206814.2017.1378131","article-title":"Stone coal in China: A review","volume":"60","author":"Dai","year":"2018","journal-title":"Int. Geol. Rev."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1007\/s11053-022-10016-z","article-title":"Spatial Effect Analysis of Coal and Gangue Recognition Detector Based on Natural Gamma Ray Method","volume":"31","author":"Zhao","year":"2022","journal-title":"Nat. Resour. Res."},{"key":"ref_34","unstructured":"Wang, C., Bochkovskiy, A., and Liao, H. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhang, J., Zhou, K., Zhang, Y., Chen, H., and Yan, X. (2023). An Improved YOLOv5-Based Underwater Object-Detection Framework. Sensors, 23.","DOI":"10.3390\/s23073693"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cao, X., Zhang, Y., Lang, S., and Gong, Y. (2023). Swin-Transformer-Based YOLOv5 for Small-Object Detection in Remote Sensing Images. Sensors, 23.","DOI":"10.3390\/s23073634"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, S., and Xie, M. (2023). Real-Time Recognition and Localization Based on Improved YOLOv5s for Robot\u2019s Picking Clustered Fruits of Chilies. Sensors, 23.","DOI":"10.3390\/s23073408"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5140\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:43:47Z","timestamp":1760125427000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,28]]},"references-count":37,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23115140"],"URL":"https:\/\/doi.org\/10.3390\/s23115140","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,28]]}}}