{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T12:33:10Z","timestamp":1762432390058,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,16]],"date-time":"2023-12-16T00:00:00Z","timestamp":1702684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Project of Hebei Education Department","award":["QN2021405","ZD2018207","21422021173","XZ2021202"],"award-info":[{"award-number":["QN2021405","ZD2018207","21422021173","XZ2021202"]}]},{"name":"Science and Technology Research Projects of Colleges and Universities in Hebei","award":["QN2021405","ZD2018207","21422021173","XZ2021202"],"award-info":[{"award-number":["QN2021405","ZD2018207","21422021173","XZ2021202"]}]},{"name":"Handan Science and Technology Bureau Project","award":["QN2021405","ZD2018207","21422021173","XZ2021202"],"award-info":[{"award-number":["QN2021405","ZD2018207","21422021173","XZ2021202"]}]},{"name":"Handan University school-level project","award":["QN2021405","ZD2018207","21422021173","XZ2021202"],"award-info":[{"award-number":["QN2021405","ZD2018207","21422021173","XZ2021202"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Currently, aeroplane images captured by camera sensors are characterized by their small size and intricate backgrounds, posing a challenge for existing deep learning algorithms in effectively detecting small targets. This paper incorporates the RFBNet (a coordinate attention mechanism) and the SIOU loss function into the YOLOv5 algorithm to address this issue. The result is developing the model for aeroplane and undercarriage detection. The primary goal is to synergize camera sensors with deep learning algorithms, improving image capture precision. YOLOv5-RSC enhances three aspects: firstly, it introduces the receptive field block based on the backbone network, increasing the size of the receptive field of the feature map, enhancing the connection between shallow and deep feature maps, and further improving the model\u2019s utilization of feature information. Secondly, the coordinate attention mechanism is added to the feature fusion network to assist the model in more accurately locating the targets of interest, considering attention in the channel and spatial dimensions. This enhances the model\u2019s attention to key information and improves detection precision. Finally, the SIoU bounding box loss function is adopted to address the issue of IoU\u2019s insensitivity to scale and increase the speed of model bounding box convergence. Subsequently, the Basler camera experimental platform was constructed for experimental verification. The results demonstrate that the AP values of the YOLOv5-RSC detection model for aeroplane and undercarriage are 92.4% and 80.5%, respectively. The mAP value is 86.4%, which is 2.0%, 5.4%, and 3.7% higher than the original YOLOv5 algorithm, respectively, with a detection speed reaching 89.2 FPS. These findings indicate that the model exhibits high detection precision and speed, providing a valuable reference for aeroplane undercarriage detection.<\/jats:p>","DOI":"10.3390\/s23249861","type":"journal-article","created":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T11:28:07Z","timestamp":1702898887000},"page":"9861","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Real-Time Detection of an Undercarriage Based on Receptive Field Blocks and Coordinate Attention"],"prefix":"10.3390","volume":"23","author":[{"given":"Ruizhen","family":"Gao","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering and Equipment, Hebei University of Engineering, Handan 056038, China"},{"name":"Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, Hebei University of Engineering, Handan 056038, China"},{"name":"Collaborative Innovation Center for Modern Equipment Manufacturing of Jinan New Area (Hebei), Handan 056038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ya\u2019nan","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Equipment, Hebei University of Engineering, Handan 056038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyue","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Equipment, Hebei University of Engineering, Handan 056038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baihua","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Loughborough University, Loughborough LE11 3TU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingjun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Equipment, Hebei University of Engineering, Handan 056038, China"},{"name":"Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, Hebei University of Engineering, Handan 056038, China"},{"name":"Collaborative Innovation Center for Modern Equipment Manufacturing of Jinan New Area (Hebei), Handan 056038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.ssci.2010.09.020","article-title":"The development of probabilistic models to estimate accident risk (due to runway overrun and landing undershoot) applicable to the design and construction of runway safety areas","volume":"49","author":"Comendador","year":"2011","journal-title":"Saf. Sci."},{"key":"ref_2","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 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_4","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster r-cnn: Towards real-time object detection with region proposal networks. Proceedings of the 28th International Conference on Neural Information Processing Systems, Cambridge, MA, USA."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (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_9","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 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_11","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_12","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2751","DOI":"10.1007\/s10346-021-01694-6","article-title":"A small attentional YOLO model for landslide detection from satellite remote sensing images","volume":"18","author":"Cheng","year":"2021","journal-title":"Landslides"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bao, W., Du, X., Wang, N., Yuan, M., and Yang, X. (2022). A Defect Detection Method Based on BC-YOLO for Transmission Line Components in UAV Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14205176"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gong, H., Mu, T., Li, Q., Dai, H., Li, C., He, Z., Wang, W., Han, F., Tuniyazi, A., and Li, H. (2022). Swin-Transformer-Enabled YOLOv5 with Attention Mechanism for Small Object Detection on Satellite Images. Remote Sens., 14.","DOI":"10.3390\/rs14122861"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1080\/08839514.2021.1975391","article-title":"Deep learning based steel pipe weld defect detection","volume":"35","author":"Yang","year":"2021","journal-title":"Appl. Artif. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s00226-021-01316-3","article-title":"Edge-glued wooden panel defect detection using deep learning","volume":"56","author":"Chen","year":"2022","journal-title":"Wood Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.neucom.2019.11.023","article-title":"Deep learning in video multi-object tracking: A survey","volume":"381","author":"Ciaparrone","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1109\/TCSVT.2020.2975842","article-title":"End-to-end learning deep CRF models for multi-object tracking deep CRF models","volume":"31","author":"Xiang","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102805","DOI":"10.1016\/j.cviu.2019.102805","article-title":"A survey on deep learning based face recognition","volume":"189","author":"Guo","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_21","first-page":"9510","article-title":"A de-identification face recognition using extracted thermal features based on deep learning","volume":"20","author":"Lin","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Doniyorjon, M., Madinakhon, R., Shakhnoza, M., and Cho, Y.I. (2022). An Improved Method of Polyp Detection Using Custom YOLOv4-Tiny. Appl. Sci., 12.","DOI":"10.3390\/app122110856"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"75385","DOI":"10.1109\/ACCESS.2022.3192034","article-title":"Stbi-yolo: A real-time object detection method for lung nodule recognition","volume":"10","author":"Liu","year":"2022","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e13866","DOI":"10.1111\/jfpe.13866","article-title":"Apple target recognition method in complex environment based on improved YOLOv4","volume":"44","author":"Ji","year":"2021","journal-title":"J. Food Process Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xu, B., Cui, X., Ji, W., Yuan, H., and Wang, J. (2023). Apple grading method design and implementation for automatic grader based on improved YOLOv5. Agriculture, 13.","DOI":"10.3390\/agriculture13010124"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, Z., Yuan, J., Li, G., Wang, H., Li, X., Li, D., and Wang, X. (2023). RSI-YOLO: Object Detection Method for Remote Sensing Images Based on Improved YOLO. Sensors, 23.","DOI":"10.3390\/s23146414"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Su, Z., Yu, J., Tan, H., Wan, X., and Qi, K. (2023). MSA-YOLO: A Remote Sensing Object Detection Model Based on Multi-Scale Strip Attention. Sensors, 23.","DOI":"10.3390\/s23156811"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1958","DOI":"10.1109\/TPAMI.2008.128","article-title":"80 million tiny images: A large data set for nonparametric object and scene recognition","volume":"30","author":"Torralba","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, C., Liu, M.Y., Tuzel, O., and Xiao, J. (2016, January 20\u201324). R-CNN for small object detection. Proceedings of the Computer Vision\u2013ACCV 2016: 13th Asian Conference on Computer Vision, Taipei, Taiwan. Revised Selected Papers, Part V 13.","DOI":"10.1007\/978-3-319-54407-6"},{"key":"ref_30","unstructured":"Yaeger, L., Lyon, R., and Webb, B. (1996, January 3\u20135). Effective training of a neural network character classifier for word recognition. Proceedings of the 9th International Conference on Neural Information Processing Systems, Cambridge, MA, USA."},{"key":"ref_31","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems, Red Hook, NY, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, C., Zhang, Y., Lv, Q., Wei, S., Wang, X., Sun, X., and Dong, J. (2019, January 27\u201328). Rrnet: A hybrid detector for object detection in drone-captured images. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, Seoul, Republic of Korea.","DOI":"10.1109\/ICCVW.2019.00018"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_34","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the 27th International Conference on Neural Information Processing Systems, Cambridge, MA, USA."},{"key":"ref_35","unstructured":"Yang, Z., Liu, S., Hu, H., Wang, L., and Lin, S. (November, January 27). Reppoints: Point set representation for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7389","DOI":"10.1109\/TIP.2020.3002345","article-title":"Foveabox: Beyound anchor-based object detection","volume":"29","author":"Kong","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, S., Huang, D., and Wang , Y. (2018, January 8\u201314). Receptive field block net for accurate and fast object detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"ref_38","unstructured":"Gevorgyan, Z. (2022). SIoU loss: More powerful learning for bounding box regression. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Guo, S., Li, L., Guo, T., Cao, Y., and Li, Y. (2022). Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5. Sensors, 22.","DOI":"10.3390\/s22134933"},{"key":"ref_42","first-page":"2582288","article-title":"The Aeroplane and Undercarriage Detection Based on Attention Mechanism and Multi-Scale Features Processing","volume":"2022","author":"Gao","year":"2022","journal-title":"Mob. Inf. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/24\/9861\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:39:52Z","timestamp":1760132392000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/24\/9861"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,16]]},"references-count":42,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["s23249861"],"URL":"https:\/\/doi.org\/10.3390\/s23249861","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,12,16]]}}}