{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:38:28Z","timestamp":1760240308388,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,5,5]],"date-time":"2019-05-05T00:00:00Z","timestamp":1557014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Program of National Natural Science Foundation of China","award":["61751204"],"award-info":[{"award-number":["61751204"]}]},{"name":"National Outstanding Youth Science Program of National Natural Science Foundation of China","award":["61625202"],"award-info":[{"award-number":["61625202"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>When measurement rates grow, most Compressive Sensing (CS) methods suffer from an increase in overheads of transmission and storage of CS measurements, while reconstruction quality degrades appreciably when measurement rates reduce. To solve these problems in real scenarios such as large-scale distributed surveillance systems, we propose a low-cost image CS approach called MRCS for object detection. It predicts key objects using the proposed MYOLO3 detector, and then samples the regions of the key objects as well as other regions using multiple measurement rates to reduce the size of sampled CS measurements. It also stores and transmits half-precision CS measurements to further reduce the required transmission bandwidth and storage space. Comprehensive evaluations demonstrate that MYOLO3 is a smaller and improved object detector for resource-limited hardware devices such as surveillance cameras and aerial drones. They also suggest that MRCS significantly reduces the required transmission bandwidth and storage space by declining the size of CS measurements, e.g., mean Compression Ratios (mCR) achieves 1.43\u201322.92 on the VOC-pbc dataset. Notably, MRCS further reduces the size of CS measurements by half-precision representations. Subsequently, the required transmission bandwidth and storage space are reduced by one half as compared to the counterparts represented with single-precision floats. Moreover, it also substantially enhances the usability of object detection on reconstructed images with half-precision CS measurements and multiple measurement rates as compared to its counterpart, using a single low measurement rate.<\/jats:p>","DOI":"10.3390\/s19092079","type":"journal-article","created":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T11:22:35Z","timestamp":1557400955000},"page":"2079","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Low-Cost Image Compressive Sensing with Multiple Measurement Rates for Object Detection"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1311-7115","authenticated-orcid":false,"given":"Longlong","family":"Liao","sequence":"first","affiliation":[{"name":"College of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenli","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Canqun","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pietrow, D., and Matuszewski, J. (2017, January 12\u201314). Objects detection and recognition system using artificial neural networks and drones. Proceedings of the Signal Processing Symposium (SPSympo), Jachranka, Poland.","DOI":"10.1109\/SPS.2017.8053689"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, C., Li, K., Teo, S.G., Chen, G., Zou, X., Yang, X., Vijay, R.C., Feng, J., and Zeng, Z. (2018, January 17\u201320). Exploiting Spatio-Temporal Correlations with Multiple 3D Convolutional Neural Networks for Citywide Vehicle Flow Prediction. Proceedings of the IEEE International Conference on Data Mining (ICDM), Singapore.","DOI":"10.1109\/ICDM.2018.00107"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2017.9","article-title":"The Emergence of Edge Computing","volume":"50","author":"Satyanarayanan","year":"2017","journal-title":"Computer"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/MSP.2007.914730","article-title":"Single-pixel imaging via compressive sampling","volume":"25","author":"Duarte","year":"2008","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3340","DOI":"10.1109\/TSMC.2016.2578465","article-title":"Active Compressive Sensing via Pyroelectric Infrared Sensor for Human Situation Recognition","volume":"47","author":"Ma","year":"2017","journal-title":"IEEE Trans. Syst. Man. Cybern. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cho, S., Kim, D.H., and Park, Y.W. (2017, January 18\u201321). Learning drone-control actions in surveillance videos. Proceedings of the 17th International Conference on Control, Automation and Systems (ICCAS), Jeju, Korea.","DOI":"10.23919\/ICCAS.2017.8204319"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/LSP.2016.2527680","article-title":"Compressive Sensing Reconstruction of Correlated Images Using Joint Regularization","volume":"23","author":"Chang","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1109\/TMM.2017.2654123","article-title":"Distributed Compressive Sensing for Cloud-Based Wireless Image Transmission","volume":"19","author":"Song","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1755003","DOI":"10.1142\/S0218001417550035","article-title":"Multiple Object Detection and Tracking in Complex Background","volume":"31","author":"Ma","year":"2017","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1555013","DOI":"10.1142\/S0218001415550137","article-title":"Salient Object Detection via Nonlocal Diffusion Tensor","volume":"29","author":"Zhang","year":"2015","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1856014","DOI":"10.1142\/S0218001418560141","article-title":"Real-Time Pedestrian Detection Using Convolutional Neural Networks","volume":"32","author":"Kuang","year":"2018","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1950004","DOI":"10.1142\/S0218001419500046","article-title":"Adaptive GMM and BP Neural Network Hybrid Method for Moving Objects Detection in Complex Scenes","volume":"33","author":"Ou","year":"2019","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wen, L., Bian, X., Lei, Z., and Li, S.Z. (2018, January 18\u201322). Single-Shot Refinement Neural Network for Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00442"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hu, Q., and Zhai, L. (2019). RGB-D Image Multi-Target Detection Method Based on 3D DSF R-CNN. Int. J. Pattern Recognit. Artif. Intell., 1954026.","DOI":"10.1142\/S0218001419540260"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1109\/TIFS.2017.2766583","article-title":"An Ensemble CNN2ELM for Age Estimation","volume":"13","author":"Duan","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 24\u201327). 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_17","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201312). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Boston, MA, USA.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_18","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in Neural Information Processing Systems (NIPS), Curran Associates, Inc."},{"key":"ref_19","unstructured":"Dai, J., Li, Y., He, K., and Sun, J. (2016). R-FCN: Object Detection via Region-based Fully Convolutional Networks. Advances in Neural Information Processing Systems 29, Curran Associates, Inc."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lin, T., 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 (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201322). Cascade R-CNN: Delving Into High Quality Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). Ssd: Single shot multibox detector. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the The 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","doi-asserted-by":"crossref","first-page":"1850025","DOI":"10.1142\/S0218001418500258","article-title":"Vehicle Driving Direction Control Based on Compressed Network","volume":"32","author":"Yang","year":"2018","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_28","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (July, January 26). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L. (2018, January 18\u201322). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3618","DOI":"10.1109\/TIP.2014.2329449","article-title":"Compressive Sensing via Nonlocal Low-Rank Regularization","volume":"23","author":"Dong","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1109\/LSP.2013.2280571","article-title":"Iterative Directional Total Variation Refinement for Compressive Sensing Image Reconstruction","volume":"20","author":"Fei","year":"2013","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_32","unstructured":"Metzler, C.A., Mousavi, A., and Baraniuk, R.G. (2017). Learned D-AMP: Principled Neural network based compressive image recovery. Advances in Neural Information Processing Systems (NIPS 2017), Curran Associates, Inc."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5117","DOI":"10.1109\/TIT.2016.2556683","article-title":"From Denoising to Compressed Sensing","volume":"62","author":"Metzler","year":"2016","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3122","DOI":"10.1016\/j.sigpro.2010.05.016","article-title":"Compressed sensing of color images","volume":"90","author":"Majumdar","year":"2010","journal-title":"Signal Process."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mousavi, A., Patel, A.B., and Baraniuk, R.G. (October2015, January 29). A deep learning approach to structured signal recovery. Proceedings of the 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA.","DOI":"10.1109\/ALLERTON.2015.7447163"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Mousavi, A., and Baraniuk, R.G. (2017, January 5\u20139). Learning to invert: Signal recovery via Deep Convolutional Networks. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952561"},{"key":"ref_37","unstructured":"Kulkarni, K., Lohit, S., Turaga, P., Kerviche, R., and Ashok, A. (July, January 26). ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Bernard, G. (2018, January 18\u201322). ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00196"},{"key":"ref_39","unstructured":"Yao, H., Dai, F., Zhang, D., Ma, Y., Zhang, S., Zhang, Y., and Tian, Q. (2017). DR2-net: Deep residual reconstruction network for image compressive sensing. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Han, D., Kim, J., and Kim, J. (2017, January 21\u201326). Deep Pyramidal Residual Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.668"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Neubeck, A., and Van Gool, L. (2006, January 20\u201324). Efficient Non-Maximum Suppression. Proceedings of the18th International Conference on Pattern Recognition (ICPR\u201906), Hong Kong, China.","DOI":"10.1109\/ICPR.2006.479"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Nandakumar, S.R., Gallo, M.L., Boybat, I., Rajendran, B., Sebastian, A., and Eleftheriou, E. (2018, January 27\u201330). Mixed-precision architecture based on computational memory for training deep neural networks. Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), Florence, Italy.","DOI":"10.1109\/ISCAS.2018.8351656"},{"key":"ref_43","unstructured":"Micikevicius, P., Narang, S., Alben, J., Diamos, G.F., Elsen, E., Garca, D., Ginsburg, B., Houston, M., Kuchaiev, O., Venkatesh, G., and Wu, H. (May, January 30). Mixed Precision Training. Proceedings of the 6th International Conference on Learning Representations (ICLR), Vancouver, BC, Canada."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Boufounos, P.T., Jacques, L., Krahmer, F., and Saab, R. (2015). Quantization and Compressive Sensing. Compressed Sensing and its Applications: MATHEON Workshop 2013, Birkh\u00e4user, Cham.","DOI":"10.1007\/978-3-319-16042-9_7"},{"key":"ref_45","unstructured":"Merve G\u00fcrel, N., Kara, K., Stojanov, A., Smith, T., Alistarh, D., P\u00fcschel, M., and Zhang, C. (2018). Compressive Sensing with Low Precision Data Representation: Theory and Applications. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Cerone, V., Fosson, S.M., and Regruto, D. (2019). A linear programming approach to sparse linear regression with quantized data. arXiv.","DOI":"10.23919\/ACC.2019.8815117"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Liao, L., Li, K., Li, K., Yang, C., and Tian, Q. (2018, January 13\u201316). UHCL-Darknet: An OpenCL-based Deep Neural Network Framework for Heterogeneous Multi-\/Many-core Clusters. Proceedings of the 47th International Conference on Parallel Processing, Eugene, OR, USA.","DOI":"10.1145\/3225058.3225107"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Jonathan, L., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 3\u20137). Caffe: Convolutional Architecture for Fast Feature Embedding. Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The Pascal Visual Object Classes Challenge: A Retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Bai, S., Bai, X., Tian, Q., and Latecki, L.J. (2018). Regularized Diffusion Process on Bidirectional Context for Object Retrieval. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2018.2828815"},{"key":"ref_51","unstructured":"Wang, J., Bohn, T., and Ling, C. (2018). Pelee: A Real-Time Object Detection System on Mobile Devices. Advances in Neural Information Processing Systems 31, Curran Associates, Inc."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/9\/2079\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:49:11Z","timestamp":1760186951000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/9\/2079"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,5]]},"references-count":51,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["s19092079"],"URL":"https:\/\/doi.org\/10.3390\/s19092079","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,5,5]]}}}