{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T13:35:05Z","timestamp":1762522505291,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T00:00:00Z","timestamp":1602806400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Key R&amp;D Program of China","award":["2016YFB1200100"],"award-info":[{"award-number":["2016YFB1200100"]}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61502354, 91638301, 61501413, 61671332, 41501505"],"award-info":[{"award-number":["61502354, 91638301, 61501413, 61671332, 41501505"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hubei Technology Innovation Project","award":["2019AAA045"],"award-info":[{"award-number":["2019AAA045"]}]},{"name":"The central government guides local science and technology development special projects","award":["2018ZYYD059"],"award-info":[{"award-number":["2018ZYYD059"]}]},{"name":"the Natural Science Foundation of Hubei Province of China","award":["2015CFB451, 2014CFA130, 2012FFA099, 2012FFA134, 2013CF125"],"award-info":[{"award-number":["2015CFB451, 2014CFA130, 2012FFA099, 2012FFA134, 2013CF125"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pedestrian detection is an essential problem of computer vision, which has achieved tremendous success under controllable conditions using visible light imaging sensors in recent years. However, most of them do not consider low-light environments which are very common in real-world applications. In this paper, we propose a novel pedestrian detection algorithm using multi-task learning to address this challenge in low-light environments. Specifically, the proposed multi-task learning method is different from the most commonly used multi-task learning method\u2014the parameter sharing mechanism\u2014in deep learning. We design a novel multi-task learning method with feature-level fusion and a sharing mechanism. The proposed approach contains three parts: an image relighting subnetwork, a pedestrian detection subnetwork, and a feature-level multi-task fusion learning module. The image relighting subnetwork adjusts the low-light image quality for detection, the pedestrian detection subnetwork learns enhanced features for prediction, and the feature-level multi-task fusion learning module fuses and shares features among component networks for boosting image relighting and detection performance simultaneously. Experimental results show that the proposed approach consistently and significantly improves the performance of pedestrian detection on low-light images obtained by visible light imaging sensor.<\/jats:p>","DOI":"10.3390\/s20205852","type":"journal-article","created":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T08:56:48Z","timestamp":1602838608000},"page":"5852","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Seeing Pedestrian in the Dark via Multi-Task Feature Fusing-Sharing Learning for Imaging Sensors"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2594-2574","authenticated-orcid":false,"given":"Yuanzhi","family":"Wang","sequence":"first","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430073, China"}]},{"given":"Tao","family":"Lu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430073, China"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Yuntao","family":"Wu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430073, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,16]]},"reference":[{"key":"ref_1","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_2","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_3","unstructured":"Ren, S., He, K., Ross, G., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the Advances in Neural Information Processing Systems 28, Montreal, QC, Canada."},{"key":"ref_4","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 European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_5","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_6","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1946","DOI":"10.1016\/j.cja.2019.03.021","article-title":"A novel visual attention method for target detection from SAR images","volume":"32","author":"GAO","year":"2019","journal-title":"Chin. J. Aeronaut."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1109\/ACCESS.2017.2777444","article-title":"Visual Saliency Modeling for River Detection in High-Resolution SAR Imagery","volume":"6","author":"Gao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2593","DOI":"10.1109\/TMM.2018.2865686","article-title":"SeaShips: A Large-Scale Precisely Annotated Dataset for Ship Detection","volume":"20","author":"Shao","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sermanet, P., Kavukcuoglu, K., Chintala, S., and Lecun, Y. (2013, January 9). Pedestrian Detection with Unsupervised Multi-stage Feature Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, Oregon.","DOI":"10.1109\/CVPR.2013.465"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2913","DOI":"10.1109\/TITS.2018.2869087","article-title":"Differential Features for Pedestrian Detection: A Taylor Series Perspective","volume":"20","author":"Shen","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Paolanti, M., Romeo, L., Liciotti, D., Cenci, A., Frontoni, E., and Zingaretti, P. (2018). Person Re-Identification with RGB-D Camera in Top-View Configuration through Multiple Nearest Neighbor Classifiers and Neighborhood Component Features Selection. Sensors, 18.","DOI":"10.3390\/s18103471"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, W., Liao, S., Hu, W., Liang, X., and Chen, X. (2018, January 8\u201314). Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization Fitting. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_38"},{"key":"ref_14","unstructured":"Zhou, X., Wang, D., and Kr\u00e4henb\u00fchl, P. (2019). Objects as Points. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, W., Liao, S., Ren, W., Hu, W., and Yu, Y. (2019, January 15\u201320). High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00533"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Law, H., and Deng, J. (2018, January 8\u201314). CornerNet: Detecting Objects as Paired Keypoints. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"ref_17","unstructured":"Law, H., Teng, Y., Russakovsky, O., and Deng, J. (2019). CornerNet-Lite: Efficient Keypoint Based Object Detection. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kruthiventi, S.S.S., Sahay, P., and Biswal, R. (2017, January 17\u201320). Low-light pedestrian detection from RGB images using multi-modal knowledge distillation. Proceedings of the IEEE International Conference on Image Processing, Beijing, China.","DOI":"10.1109\/ICIP.2017.8297075"},{"key":"ref_19","unstructured":"Ruder, S. (2017). An Overview of Multi-Task Learning in Deep Neural Networks. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Collobert, R., and Weston, J. (2008, January 5\u20139). A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning. Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland.","DOI":"10.1145\/1390156.1390177"},{"key":"ref_21","unstructured":"Ramsundar, B., Kearnes, S.M., Riley, P., Webster, D., Konerding, D.E., and Pande, V.S. (2015). Massively Multitask Networks for Drug Discovery. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1023\/A:1007379606734","article-title":"Multitask Learning","volume":"28","author":"Caruana","year":"1997","journal-title":"Mach. Learn."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Caruana, R. (1993, January 27\u201329). Multitask Learning: A Knowledge-Based Source of Inductive Bias. Proceedings of the Tenth International Conference on Machine Learning, San Francisco, CA, USA.","DOI":"10.1016\/B978-1-55860-307-3.50012-5"},{"key":"ref_24","unstructured":"Long, M., and Wang, J. (2015). Learning Multiple Tasks with Deep Relationship Networks. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Misra, I., Shrivastava, A., Gupta, A., and Hebert, M. (2016, January 27\u201330). Cross-Stitch Networks for Multi-Task Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.433"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gao, Y., Ma, J., Zhao, M., Liu, W., and Yuille, A.L. (2019, January 21\u201325). NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00332"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gao, Y., Bai, H., Jie, Z., Ma, J., Jia, K., and Liu, W. (2020, January 16\u201318). MTL-NAS: Task-Agnostic Neural Architecture Search Towards General-Purpose Multi-Task Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01156"},{"key":"ref_28","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 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_29","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5799","DOI":"10.1109\/TGRS.2019.2902431","article-title":"Edge-Enhanced GAN for Remote Sensing Image Superresolution","volume":"57","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2530","DOI":"10.1109\/TIP.2018.2887017","article-title":"Multi-Memory Convolutional Neural Network for Video Super-Resolution","volume":"28","author":"Wang","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3275","DOI":"10.1109\/TNNLS.2018.2890550","article-title":"Separability and Compactness Network for Image Recognition and Superresolution","volume":"30","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2503","DOI":"10.1109\/TCSVT.2019.2925844","article-title":"Multi-Temporal Ultra Dense Memory Network for Video Super-Resolution","volume":"30","author":"Yi","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Dollar, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., 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_36","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., and Le, Q.V. (2018, January 18\u201323). Learning Transferable Architectures for Scalable Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, J.J., Hou, Q., Cheng, M.M., Wang, C., and Feng, J. (2020, January 13\u201319). Improving Convolutional Networks with Self-Calibrated Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01011"},{"key":"ref_40","unstructured":"Zhang, H., Wu, C., Zhang, Z., Zhu, Y., Zhang, Z.L., Lin, H., Sun, Y., He, T., Mueller, J., and Manmatha, R. (2020). ResNeSt: Split-Attention Networks. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., and Yang, J. (2019, January 15\u201321). Selective Kernel Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00060"},{"key":"ref_42","unstructured":"Wei, C., Wang, W., Yang, W., and Liu, J. (2018, January 3\u20136). Deep Retinex Decomposition for Low-Light Enhancement. Proceedings of the British Machine Vision Conference. British Machine Vision Association, Newcastle, UK."},{"key":"ref_43","unstructured":"Alejandro, N., and Jia, D. (2017). Pixels to Graphs by Associative Embedding. Advances in Neural Information Processing Systems 31, Curran Associates, Inc."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, S., Benenson, R., and Schiele, B. (2017, January 21\u201326). CityPersons: A Diverse Dataset for Pedestrian Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.474"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1109\/TPAMI.2011.155","article-title":"Pedestrian Detection: An Evaluation of the State of the Art","volume":"34","author":"Dollar","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/20\/5852\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:22:21Z","timestamp":1760178141000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/20\/5852"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,16]]},"references-count":45,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["s20205852"],"URL":"https:\/\/doi.org\/10.3390\/s20205852","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,10,16]]}}}