{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:29:29Z","timestamp":1784302169208,"version":"3.55.0"},"reference-count":38,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T00:00:00Z","timestamp":1647993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, aviation security has become an important area of concern as foreign object debris (FOD) on the airport pavement has a huge potential risk to aircraft during takeoff and landing. Therefore, accurate detection of FOD is important to ensure aircraft flight safety. This paper proposes a novel method to detect FOD based on random forest. The complexity of information in airfield pavement images and the variability of FOD make FOD features difficult to design manually. To overcome this challenge, this study designs the pixel visual feature (PVF), in which weight and receptive field are determined through learning to obtain the optimal PVF. Then, the framework of random forest employing the optimal PVF to segment FOD is proposed. The effectiveness of the proposed method is demonstrated on the FOD dataset. The results show that compared with the original random forest and the deep learning method of Deeplabv3+, the proposed method is superior in precision and recall for FOD detection. This work aims to improve the accuracy of FOD detection and provide a reference for researchers interested in FOD detection in aviation.<\/jats:p>","DOI":"10.3390\/s22072463","type":"journal-article","created":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T22:08:06Z","timestamp":1648073286000},"page":"2463","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Foreign Object Debris Detection for Optical Imaging Sensors Based on Random Forest"],"prefix":"10.3390","volume":"22","author":[{"given":"Ying","family":"Jing","sequence":"first","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wentao","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaihan","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,23]]},"reference":[{"key":"ref_1","unstructured":"Federal Aviation Administration (FAA) (2010). Foreign Object Debris (Fod) Management, Document Advisory Circular(ac) 150\/5220-24."},{"key":"ref_2","unstructured":"(2019, June 01). Air France Flight 4590. Available online: http:\/\/en.wikipedia.org\/wiki\/Air_France_Flight_4590."},{"key":"ref_3","unstructured":"(2020, December 21). Tarsier: Automatic Runway Fod Detection System. Available online: https:\/\/www.moog.com\/markets\/aircraft\/tarsierfod.html."},{"key":"ref_4","unstructured":"(2020, December 12). Xsight: Advanced Radar Furthermore, Optic Sensors for Fod Detection and Homeland Security. Available online: https:\/\/www.xsightsys.com\/index.php\/fodetect\/."},{"key":"ref_5","unstructured":"(2020, December 27). Fod Finder: The Total Solution for Fod Control. Available online: http:\/\/www.fodfinder.com\/."},{"key":"ref_6","unstructured":"(2021, January 10). iferret on Scratch. Available online: http:\/\/www.stratechsystems.com."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ni, P., Miao, C., Tang, H., Jiang, M., and Wu, W. (2020). Small foreign object debris detection for millimeter-wave radar based on power spectrum features. Sensors, 20.","DOI":"10.3390\/s20082316"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhong, J., Gou, X., Shu, Q., Liu, X., and Zeng, Q. (2021). A fod detection approach on millimeter-wave radar sensors based on optimal vmd and svdd. Sensors, 21.","DOI":"10.3390\/s21030997"},{"key":"ref_9","first-page":"1","article-title":"Improved region growth algorithm applicable to detection of foreign object debris on airport runway","volume":"43","author":"Zheng","year":"2020","journal-title":"Mod. Electron. Tech."},{"key":"ref_10","unstructured":"Xu, Q.Y., Ning, H.S., and Chen, W.S. (2009, January 11\u201312). Video-based foreign object debris detection. Proceedings of the IEEE International Workshop on Imaging Systems and Techniques, Shenzhen, China."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1108\/00022661111138648","article-title":"Foreign object debris surveillance network for runway security","volume":"83","author":"Chen","year":"2011","journal-title":"Aircr. Eng. Aerospace Technol."},{"key":"ref_12","unstructured":"Zhang, J.R., Guo, Y.Y., and Yang, G.Q. (2010, January 22\u201324). Airport runway debris detection based on weighted fuzzy morphology algorithm. Proceedings of the International Conference on Computer Application and System Modeling, Taiyuan, China."},{"key":"ref_13","unstructured":"Zhang, K., Cui, D.S., Zhang, Y., Cao, C.H., Xiao, F., and Huang, G.B. (2017, January 11\u201314). Classification of foreign object debris using integrated visual features and extreme learning machine. Proceedings of the CCF Chinese Conference on Computer Vision, Tianjin, China."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.12733\/jics20101633","article-title":"Research of fod recognition based on gabor wavelets and svm classification","volume":"10","author":"Niu","year":"2013","journal-title":"J. Inform. Comput. Sci."},{"key":"ref_15","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"ref_16","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., and Sang, N. (2018, January 8\u201314). Bisenet: Bilateral segmentation network for real-time semantic segmentation. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cao, X., Wang, P., Meng, C., Bai, X., Gong, G., Liu, M., and Qi, J. (2018). Region based CNN for foreign object debris detection on airfield pavement. Sensors, 18.","DOI":"10.3390\/s18030737"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, P., and Li, H. (2020, January 27\u201329). Research on fod detection for airport runway based on yolov3. Proceedings of the 39th Chinese Control Conference (CCC), Shenyang, China.","DOI":"10.23919\/CCC50068.2020.9188724"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gao, Q., Hong, R., Chen, Y., and Lei, J. (2021, January 28\u201330). Research on foreign object debris detection in airport runway based on semantic segmentation. Proceedings of the 2nd International Conference on Computing and Data Science, Stanford, CA, USA.","DOI":"10.1145\/3448734.3450860"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shotton, J., Johnson, M., and Cipolla, R. (2008, January 23\u201328). Semantic texton forests for image categorization and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587503"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Schroff, F., Criminisi, A., and Zisserman, A. (2008, January 1\u20134). Object Class Segmentation using Random Forests. Proceedings of the British Machine Vision Conference, Leeds, UK.","DOI":"10.5244\/C.22.54"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2821","DOI":"10.1109\/TPAMI.2012.241","article-title":"Efficient human pose estimation from single depth images","volume":"35","author":"Shotton","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., and Blake, A. (2011, January 20\u201325). Real-time human pose recognition in parts from single depth images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995316"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential evolution\u2014A simple and efficient heuristic for global optimization over continuous spaces","volume":"11","author":"Storn","year":"1997","journal-title":"J. Glob. Optim."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3542","DOI":"10.1109\/TIP.2019.2905081","article-title":"Random forest with learned representations for semantic segmentation","volume":"28","author":"Kang","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","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 International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_32","unstructured":"CAAC (2009). Manual on Preventing Foreign Object Debris (FOD)."},{"key":"ref_33","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Dwibedi, D., Misra, I., and Hebert, M. (2017, January 22\u201329). Cut, paste and learn: Surprisingly easy synthesis for instance detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.146"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"107929","DOI":"10.1016\/j.patcog.2021.107929","article-title":"STDnet-ST: Spatio-temporal ConvNet for small object detection","volume":"116","author":"Bosquet","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Wu, Y., He, K., and Girshick, R. (2020, January 13\u201319). Pointrend: Image segmentation as rendering. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00982"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ren, Y., Zhu, C., and Xiao, S. (2018). Small object detection in optical remote sensing images via modified faster R-CNN. Appl. Sci., 8.","DOI":"10.3390\/app8050813"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2463\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:41:28Z","timestamp":1760136088000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2463"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,23]]},"references-count":38,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["s22072463"],"URL":"https:\/\/doi.org\/10.3390\/s22072463","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,23]]}}}