{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:31:15Z","timestamp":1760236275918,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,7]],"date-time":"2021-11-07T00:00:00Z","timestamp":1636243200000},"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>The aim of this paper is to distinguish the vehicle detection and count the class number in each classification from the inputs. We proposed the use of Fuzzy Guided Scale Choice (FGSC)-based SSD deep neural network architecture for vehicle detection and class counting with parameter optimization. The \u2018FGSC\u2019 blocks are integrated into the convolutional layers of the model, which emphasize essential features while ignoring less important ones that are not significant for the operation. We created the passing detection lines and class counting windows and connected them with the proposed FGSC-SSD deep neural network model. The \u2018FGSC\u2019 blocks in the convolution layer emphasize essential features and find out unnecessary features by using the scale choice method at the training stage and eliminate that significant speedup of the model. In addition, FGSC blocks avoided many unusable parameters in the saturation interval and improved the performance efficiency. In addition, the Fuzzy Sigmoid Function (FSF) increases the activation interval through fuzzy logic. While performing operations, the FGSC-SSD model reduces the computational complexity of convolutional layers and their parameters. As a result, the model tested Frames Per Second (FPS) on edge artificial intelligence (AI) and reached a real-time processing speed of 38.4 and an accuracy rate of more than 94%. Therefore, this work might be considered an improvement to the traffic monitoring approach by using edge AI applications.<\/jats:p>","DOI":"10.3390\/s21217399","type":"journal-article","created":{"date-parts":[[2021,11,7]],"date-time":"2021-11-07T20:42:54Z","timestamp":1636317774000},"page":"7399","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting"],"prefix":"10.3390","volume":"21","author":[{"given":"Ming-Hwa","family":"Sheu","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, National Yunlin University of Science and Technology, Douliu 64002, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6271-1022","authenticated-orcid":false,"given":"S. M. Salahuddin","family":"Morsalin","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Yunlin University of Science and Technology, Douliu 64002, Taiwan"}]},{"given":"Jia-Xiang","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Yunlin University of Science and Technology, Douliu 64002, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9828-0773","authenticated-orcid":false,"given":"Shih-Chang","family":"Hsia","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Yunlin University of Science and Technology, Douliu 64002, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8709-2715","authenticated-orcid":false,"given":"Cheng-Jian","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411030, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9476-8130","authenticated-orcid":false,"given":"Chuan-Yu","family":"Chang","sequence":"additional","affiliation":[{"name":"Computer Science and Information Engineering, National Yunlin University of Science and Technology, Douliu 64002, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.trpro.2021.02.038","article-title":"Changes in Road Traffic Caused by the Declaration of a State of Emergency in the Czech Republic\u2014A Case Study","volume":"53","author":"Ladislav","year":"2021","journal-title":"Transp. Res. Procedia"},{"key":"ref_2","first-page":"1789","article-title":"Intelligent Vehicle Counting and Classification Sensor for Real-Time Traffic Surveillance","volume":"19","author":"Walid","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_3","unstructured":"Cheng, H.Y., and Hsin, M.T. (2019, January 4\u20136). Vehicle Counting and Speed Estimation with RFID Backscatter Signal. Proceedings of the IEEE Vehicular Networking Conference (VNC), Los Angeles, CA, USA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1109\/JSYST.2008.921287","article-title":"An RFID-Enabled Road Pricing System for Transportation","volume":"2","author":"David","year":"2008","journal-title":"IEEE Syst. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2675","DOI":"10.1109\/TITS.2017.2757040","article-title":"Synergizing Appearance and Motion with Low-Rank Representation for Vehicle Counting and Traffic Flow Analysis","volume":"19","author":"Zhi","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1049\/iet-its.2017.0047","article-title":"Real-time Vehicle Detection and Counting in Complex Traffic Scenes Using Background Subtraction Model with Low-rank Decomposition","volume":"12","author":"Honghong","year":"2018","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1049\/iet-its.2018.5005","article-title":"Fast single-shot multi-box detector and its application on a vehicle counting system","volume":"12","author":"Lili","year":"2018","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hou, X., and Zhang, L. (2007, January 17\u201322). Saliency Detection: A Spectral Residual Approach. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383267"},{"key":"ref_9","unstructured":"Yi, L., Qiang, Z., Dingwen, Z., and Jungong, H. (November, January 27). Employing Deep Part-Object Relationships for Salient Object Detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_10","unstructured":"Dingwen, Z., Haibin, T., and Jungong, H. (2020, January 6\u201312). Few-Cost Salient Object Detection with Adversarial-Paced Learning. Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS), Vancouver, BC, Canada."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.1109\/TPAMI.2019.2900649","article-title":"Synthesizing Supervision for Learning Deep Saliency Network without Human Annotation","volume":"42","author":"Dingwen","year":"2020","journal-title":"IEEE Trans. Patte. Analy. Machi. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/MSP.2017.2749125","article-title":"Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey","volume":"35","author":"Junwei","year":"2018","journal-title":"IEEE Signal Process. Magaz."},{"key":"ref_13","unstructured":"Wen, L., Dragomir, A., Dumitru, E., Christian, S., Scott, R., Cheng, Y.F., and Alexander, C.B. (2016, January 11\u201314). SSD: Single Shot MultiBox Detector. Proceedings of the 14th European Conference, Amsterdam, The Netherlands."},{"key":"ref_14","unstructured":"Christian, S., Wei, L., Yangqing, J., Pierre, S., Scott, R., Dragomir, A., Dumitru, E., Vincent, V., and Andrew, R. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA."},{"key":"ref_15","unstructured":"Alex, K., Ilya, S., and Geoffrey, E.H. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_16","unstructured":"Mu, Y., Brian, L., Haoqiang, F., and Yuning, J. (2015, January 27\u201330). Randomized Spatial Pooling in Deep Convolutional Networks for Scene Recognition. Proceedings of the IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada."},{"key":"ref_17","unstructured":"Ross, G., Jeff, D., Trevor, D., and Jitendra, M. (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."},{"key":"ref_18","unstructured":"Ross, G. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile."},{"key":"ref_19","unstructured":"Shaoqing, R., Kaiming, H., Ross, G., and Jian, S. (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, Montreal, QC, Canada."},{"key":"ref_20","unstructured":"Kaiming, H., Georgia, G., Piotr, D., and Ross, G. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1109\/TIP.2019.2938879","article-title":"ME R-CNN: Multi-Expert R-CNN for Object Detection","volume":"29","author":"Hyungtae","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8019","DOI":"10.1109\/TVT.2018.2843394","article-title":"MFR-CNN: Incorporating Multi-Scale Features and Global Information for Traffic Object Detection","volume":"67","author":"Hui","year":"2018","journal-title":"IEEE Trans. Vehicu. Techn."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/TIV.2020.3010832","article-title":"Soft-Weighted-Average Ensemble Vehicle Detection Method Based on Single-Stage and Two-Stage Deep Learning Models","volume":"6","author":"Hai","year":"2021","journal-title":"IEEE Trans. Intell. Vehic."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/TIP.2018.2865280","article-title":"Attention CoupleNet: Fully Convolutional Attention Coupling Network for Object Detection","volume":"28","author":"Yousong","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","unstructured":"Joseph, R., Santosh, D., Ross, G., and Ali, F. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_26","unstructured":"Zhishuai, Z., Siyuan, Q., Cihang, X., Wei, S., Bo, W., and Alan, L.Y. (2018, January 18\u201322). Single-Shot Object Detection with Enriched Semantics. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA."},{"key":"ref_27","unstructured":"Tsung, Y.L., Priya, G., Ross, G., Kaiming, H., and Piotr, D. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy."},{"key":"ref_28","unstructured":"Bichen, W., Alvin, W., Forrest, I., Peter, H.J., and Kurt, K. (2017, January 21\u201326). SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA."},{"key":"ref_29","unstructured":"Hei, L., and Jia, D. (2018, January 8\u201314). CornerNet: Detecting Objects as Paired Keypoints. Proceedings of the 15th European Conference, Munich, Germany."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2972","DOI":"10.1109\/TCSVT.2018.2875449","article-title":"Multi-Scale Attention Deep Neural Network for Fast Accurate Object Detection","volume":"29","author":"Kaiyou","year":"2019","journal-title":"IEEE Trans. Circus. Syst. Video Techn."},{"key":"ref_31","unstructured":"Seelam, S.K., Voruganti, P., Nandikonda, N., and Ramesh, T.K. (2020, January 6\u20138). Vehicle Detection Using Image Processing. Proceedings of the IEEE International Conference for Innovation in Technology (INOCON), Bengaluru, India."},{"key":"ref_32","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":"Sheping","year":"2020","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Palo, J., Caban, J., Kiktova, M., and Cernicky, L. (2019). The Comparison of Automatic Traffic Counting and Manual Traffic Counting. Proceedings of the IOP Conference Series: Materials Science and Engineering, IOP Publishing.","DOI":"10.1088\/1757-899X\/710\/1\/012041"},{"key":"ref_34","unstructured":"Jie, H., Li, S., and Gang, S. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lewis, F.L., and Liu, K. (1994, January 18\u201321). Towards a Paradigm for Fuzzy Logic Control. Proceedings of the Industrial Fuzzy Control and Intellige (NAFIPS\/IFIS\/NASA \u201994), San Antonio, TX, USA.","DOI":"10.1109\/IJCF.1994.375143"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","article-title":"Fuzzy sets","volume":"8","author":"Zadeh","year":"1965","journal-title":"Inf. Control"},{"key":"ref_37","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":"Mark","year":"2010","journal-title":"Int. J. Compu. Vis."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Skala, H.J. (1976). Fuzzy Concepts: Logic, Motivation, Application. Systems Theory in the Social Sciences, Birkh\u00e4user. Available online: https:\/\/doi.org\/10.1007\/978-3-0348-5495-5_13.","DOI":"10.1007\/978-3-0348-5495-5_13"},{"key":"ref_39","unstructured":"Timothy, J.R. (2010). Fuzzy Logic with Engineering Application, John Wiley & Sons Inc.. [3rd ed.]."},{"key":"ref_40","unstructured":"Chris, S., and Grimson, W.E.L. (1999, January 23\u201325). Adaptive Background Mixture Models for Real-Time Tracking. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, USA."},{"key":"ref_41","unstructured":"Jiani, X., Zhihui, W., and Daoerji, F. (2020, January 17\u201319). A Solution for Vehicle Attributes Recognition and Cross-dataset Annotation. Proceedings of the 13th International Congress on Image and Signal Processing, BioMedical Engineering, and Informatics (CISP-BMEI), Chengdu, China."},{"key":"ref_42","unstructured":"Yangqing, J., Evan, S., Jeff, D., Sergey, K., Jonathan, L., Ross, G., Sergio, G., and Trevor, D. (2014, January 3\u20137). Caffe: Convolutional Architecture for Fast Feature Embedding. Proceedings of the 22nd ACM International Conference on Multimedia, New York, NY, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"64460","DOI":"10.1109\/ACCESS.2019.2914254","article-title":"Video-based Vehicle Counting Framework","volume":"7","author":"Zhe","year":"2019","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3125","DOI":"10.1007\/s10489-020-01704-5","article-title":"Finding Every Car: A Traffic Surveillance Multi-Scale Vehicle Object Detection Method","volume":"50","author":"Qi","year":"2020","journal-title":"Appl. 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