{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T22:17:04Z","timestamp":1781734624546,"version":"3.54.5"},"reference-count":53,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,17]],"date-time":"2021-04-17T00:00:00Z","timestamp":1618617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korean Institute for Advancement of Technology (KIAT)","award":["P0002397"],"award-info":[{"award-number":["P0002397"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The development of deep learning has achieved great success in object detection, but small object detection is still a difficult and challenging task in computer vision. To address the problem, we propose an improved single-shot multibox detector (SSD) using enhanced feature map blocks (SSD-EMB). The enhanced feature map block (EMB) consists of attention stream and feature map concatenation stream. The attention stream allows the proposed model to focus on the object regions rather than background owing to channel averaging and the effectiveness of the normalization. The feature map concatenation stream provides additional semantic information to the model without degrading the detection speed. By combining the output of these two streams, the enhanced feature map, which improves the detection of a small object, is generated. Experimental results show that the proposed model has high accuracy in small object detection. The proposed model not only achieves good detection accuracy, but also has a good detection speed. The SSD-EMB achieved a mean average precision (mAP) of 80.4% on the PASCAL VOC 2007 dataset at 30 frames per second on an RTX 2080Ti graphics processing unit, an mAP of 79.9% on the VOC 2012 dataset, and an mAP of 26.6% on the MS COCO dataset.<\/jats:p>","DOI":"10.3390\/s21082842","type":"journal-article","created":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T21:59:49Z","timestamp":1618869589000},"page":"2842","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["SSD-EMB: An Improved SSD Using Enhanced Feature Map Block for Object Detection"],"prefix":"10.3390","volume":"21","author":[{"given":"Hong-Tae","family":"Choi","sequence":"first","affiliation":[{"name":"School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ho-Jun","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hoon","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sungwook","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ho-Hyun","family":"Park","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1109\/TITS.2020.2972974","article-title":"Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges","volume":"22","author":"Feng","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","unstructured":"Zhang, C., Xu, X., and Tu, D. (2018). Face Detection Using Improved Faster RCNN. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.neucom.2019.04.028","article-title":"CLU-CNNs: Object detection for medical images","volume":"350","author":"Li","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hashib, H., Leon, M., and Salaque, A.M. (2019, January 11\u201312). Object Detection Based Security System Using Machine learning algorthim and Raspberry Pi. Proceedings of the 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), Rajshahi, Bangladesh.","DOI":"10.1109\/IC4ME247184.2019.9036531"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_6","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_7","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_8","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_9","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft COCO: Common Objects in Context. Proceedings of the Computer Vision\u2014ECCV 2014, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_11","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_12","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_13","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":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","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_15","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\u2014ECCV 2016, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_16","unstructured":"Fu, C.-Y., Liu, W., Ranga, A., Tyagi, A., and Berg, A.C. (2017). DSSD: Deconvolutional Single Shot Detector. arXiv."},{"key":"ref_17","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_18","first-page":"2121","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"Duchi","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_19","unstructured":"Zeiler, M.D. (2012). ADADELTA: An Adaptive Learning Rate Method. arXiv."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Singh, B., and Davis, L.S. (2018, January 18\u201323). An analysis of scale invariance in object detection snip. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00377"},{"key":"ref_22","first-page":"9310","article-title":"SNIPER: Efficient Multi-Scale Training","volume":"Volume 31","author":"Bengio","year":"2018","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, J., Sun, J., Wang, J., and Yue, X.-G. (2020). Visual object tracking based on residual network and cascaded correlation filters. J. Ambient Intell. Humaniz. Comput.","DOI":"10.1007\/s12652-020-02572-0"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"33853","DOI":"10.1007\/s11042-019-08584-z","article-title":"Efficient dynamic domain adaptation on deep CNN","volume":"79","author":"Yang","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hwang, Y.-J., Lee, J.-G., Moon, U.-C., and Park, H.-H. (2020). SSD-TSEFFM: New SSD Using Trident Feature and Squeeze and Extraction Feature Fusion. Sensors, 20.","DOI":"10.3390\/s20133630"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"24344","DOI":"10.1109\/ACCESS.2020.2971026","article-title":"DF-SSD: An Improved SSD Object Detection Algorithm Based on DenseNet and Feature Fusion","volume":"8","author":"Zhai","year":"2020","journal-title":"IEEE Access"},{"key":"ref_27","unstructured":"Denton, E., Zaremba, W., Bruna, J., LeCun, Y., and Fergus, R. (2014). Exploiting linear structure within convolutional networks for efficient evaluation. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jaderberg, M., Vedaldi, A., and Zisserman, A. (2014, January 1\u20135). Speeding up convolutional neural networks with low rank expansions. Proceedings of the British Machine Vision Conference, Nottingham, UK.","DOI":"10.5244\/C.28.88"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Cao, J., Song, C., Song, S., Peng, S., Wang, D., Shao, Y., and Xiao, F. (2020). Front vehicle detection algorithm for smart car based on improved SSD model. Sensors, 20.","DOI":"10.3390\/s20164646"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ding, F., Zhuang, Z., Liu, Y., Jiang, D., Yan, X., and Wang, Z. (2020). Detecting defects on solid wood panels based on an improved SSD algorithm. Sensors, 20.","DOI":"10.3390\/s20185315"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The Pascal Visual Object Classes (VOC) Challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_32","first-page":"5998","article-title":"Attention is All you Need","volume":"Volume 30","author":"Guyon","year":"2017","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., and Huang, T.S. (2018, January 18\u201323). Generative image inpainting with contextual attention. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00577"},{"key":"ref_34","unstructured":"Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., and Bengio, Y. (2015, January 6\u201311). Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_35","first-page":"7354","article-title":"Self-Attention Generative Adversarial Networks","volume":"Volume 97","author":"Chaudhuri","year":"2019","journal-title":"Proceedings of the 36th International Conference on Machine Learning"},{"key":"ref_36","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_37","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201323). Non-local neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_38","unstructured":"Park, J., Woo, S., Lee, J.-Y., and Kweon, I.S. (2018). BAM: Bottleneck Attention Module. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and So Kweon, I. (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":"Choe, J., Lee, S., and Shim, H. (2020). Attention-based Dropout Layer for Weakly Supervised Single Object Localization and Semantic Segmentation. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/CVPR.2019.00232"},{"key":"ref_41","unstructured":"Gao, C., Zou, Y., and Huang, J.-B. (2018). ICAN: Instance-centric attention network for human-object interaction detection. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-end object detection with transformers. arXiv.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ning, X., Gong, K., Li, W., Zhang, L., Bai, X., and Tian, S. (2020). Feature refinement and filter network for person re-identification. IEEE Trans. Circuits Syst. Video Technol., 31.","DOI":"10.1109\/TCSVT.2020.3043026"},{"key":"ref_44","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_45","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_46","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_47","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 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"887","DOI":"10.32604\/iasc.2020.010122","article-title":"Multi-scale boxes loss for object detection in smart energy","volume":"26","author":"Dai","year":"2020","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"693","DOI":"10.32604\/iasc.2020.010103","article-title":"Object detection and fuzzy-based classification using UAV data","volume":"26","author":"Qayyum","year":"2020","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ciccone, V., Ferrante, A., and Zorzi, M. (2018, January 17\u201319). Robust identification of \u201csparse plus low-rank\u201d graphical models: An optimization approach. Proceedings of the 2018 IEEE Conference on Decision and Control (CDC), Miami, FL, USA.","DOI":"10.1109\/CDC.2018.8619796"},{"key":"ref_51","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wen, L., Bian, X., Lei, Z., and Li, S.Z. (2018, January 18\u201323). Single-shot refinement neural network for object detection. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00442"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/TCSVT.2020.2986402","article-title":"RefineDet++: Single-shot refinement neural network for object detection","volume":"31","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/8\/2842\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:49:18Z","timestamp":1760161758000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/8\/2842"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,17]]},"references-count":53,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["s21082842"],"URL":"https:\/\/doi.org\/10.3390\/s21082842","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,17]]}}}