{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:19:32Z","timestamp":1775578772298,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The object recognition concept is being widely used a result of increasing CCTV surveillance and the need for automatic object or activity detection from images or video. Increases in the use of various sensor networks have also raised the need of lightweight process frameworks. Much research has been carried out in this area, but the research scope is colossal as it deals with open-ended problems such as being able to achieve high accuracy in little time using lightweight process frameworks. Convolution Neural Networks and their variants are widely used in various computer vision activities, but most of the architectures of CNN are application-specific. There is always a need for generic architectures with better performance. This paper introduces the Dimension-Based Generic Convolution Block (DBGC), which can be used with any CNN to make the architecture generic and provide a dimension-wise selection of various height, width, and depth kernels. This single unit which uses the separable convolution concept provides multiple combinations using various dimension-based kernels. This single unit can be used for height-based, width-based, or depth-based dimensions; the same unit can even be used for height and width, width and depth, and depth and height dimensions. It can also be used for combinations involving all three dimensions of height, width, and depth. The main novelty of DBGC lies in the dimension selector block included in the proposed architecture. Proposed unoptimized kernel dimensions reduce FLOPs by around one third and also reduce the accuracy by around one half; semi-optimized kernel dimensions yield almost the same or higher accuracy with half the FLOPs of the original architecture, while optimized kernel dimensions provide 5 to 6% higher accuracy with around a 10 M reduction in FLOPs.<\/jats:p>","DOI":"10.3390\/s22051780","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T21:11:07Z","timestamp":1645737067000},"page":"1780","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["DBGC: Dimension-Based Generic Convolution Block for Object Recognition"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0001-8280-1140","authenticated-orcid":false,"given":"Chirag","family":"Patel","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Devang Patel Institute of Advance Technology and Research (DEPSTAR), Faculty of Technology and Engineering (FTE), CHARUSAT Campus, Charotar University of Science and Technology (CHARUSAT), Changa 388421, India"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0001-5247-4572","authenticated-orcid":false,"given":"Dulari","family":"Bhatt","sequence":"additional","affiliation":[{"name":"Parul University, Vadodara 382030, Gujarat, India"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0003-2433-5676","authenticated-orcid":false,"given":"Urvashi","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Devang Patel Institute of Advance Technology and Research (DEPSTAR), Faculty of Technology and Engineering (FTE), CHARUSAT Campus, Charotar University of Science and Technology (CHARUSAT), Changa 388421, India"}]},{"given":"Radhika","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Devang Patel Institute of Advance Technology and Research (DEPSTAR), Faculty of Technology and Engineering (FTE), CHARUSAT Campus, Charotar University of Science and Technology (CHARUSAT), Changa 388421, India"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-4507-1844","authenticated-orcid":false,"given":"Sharnil","family":"Pandya","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0001-6462-059X","authenticated-orcid":false,"given":"Kirit","family":"Modi","sequence":"additional","affiliation":[{"name":"Sankalchand Patel College of Engineering, Sankalchand Patel University, Visnagar 384315, India"}]},{"given":"Nagaraj","family":"Cholli","sequence":"additional","affiliation":[{"name":"Department of Information Science and Engineering, R. V. College of Engineering, Banglore 560059, India"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0001-6616-8007","authenticated-orcid":false,"given":"Akash","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Devang Patel Institute of Advance Technology and Research (DEPSTAR), Faculty of Technology and Engineering (FTE), CHARUSAT Campus, Charotar University of Science and Technology (CHARUSAT), Changa 388421, India"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-0312-6640","authenticated-orcid":false,"given":"Urvi","family":"Bhatt","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Devang Patel Institute of Advance Technology and Research (DEPSTAR), Faculty of Technology and Engineering (FTE), CHARUSAT Campus, Charotar University of Science and Technology (CHARUSAT), Changa 388421, India"}]},{"given":"Muhammad Ahmed","family":"Khan","sequence":"additional","affiliation":[{"name":"DTU Health Tech Department of Health Technology, 247 99 Lyngby, Denmark"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-3703-4904","authenticated-orcid":false,"given":"Shubhankar","family":"Majumdar","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, National Institute of Technology, Bijni Complex, Laitumkhrah, Shillong 793003, Meghalaya, India"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-7093-7005","authenticated-orcid":false,"given":"Mohd","family":"Zuhair","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-2318-3183","authenticated-orcid":false,"given":"Khushi","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Devang Patel Institute of Advance Technology and Research (DEPSTAR), Faculty of Technology and Engineering (FTE), CHARUSAT Campus, Charotar University of Science and Technology (CHARUSAT), Changa 388421, India"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0003-2052-1121","authenticated-orcid":false,"given":"Syed Aziz","family":"Shah","sequence":"additional","affiliation":[{"name":"Healthcare Technology and Innovation Theme, Faculty Research Centre for Intelligent Healthcare, Coventry University, Richard Crossman Building, Coventry CV1 5RW, UK"}]},{"given":"Hemant","family":"Ghayvat","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Technology, Linnaeus University, P G Vejdes v\u00e4g, 351 95 V\u00e4xj\u00f6, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., and Ghayvat, H. (2021). CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope. Electronics, 10.","DOI":"10.3390\/electronics10202470"},{"key":"ref_2","first-page":"11","article-title":"Survey On Various Intelligent Traffic Management Schemes For Emergency Vehicles","volume":"1","author":"Bhatt","year":"2013","journal-title":"Int. J. Recent Innov."},{"key":"ref_3","unstructured":"Garg, S., Patel, C., Tank, H., and Ukani, V. (2016, January 11\u201312). Efficient Vehicle Detection and Classification for Traffic Surveillance System. Proceedings of the International Conference on Advances in Computing and Data Sciences, Ghaziabad, India."},{"key":"ref_4","first-page":"2","article-title":"Object Detection and Segmentation using Local and Global Property","volume":"2","author":"Patel","year":"2012","journal-title":"Int. J. Emerg. Technol. Sci. Eng."},{"key":"ref_5","first-page":"309","article-title":"Comparative analysis of traditional methods for moving object detection in video sequence","volume":"6","author":"Garg","year":"2015","journal-title":"Int. J. Comput. Sci. Commun."},{"key":"ref_6","first-page":"13","article-title":"Top-down and bottom-up cues based moving object detection for varied background video sequences","volume":"2014","author":"Garg","year":"2014","journal-title":"Adv. Multimed."},{"key":"ref_7","first-page":"816","article-title":"A survey on drivers drowsiness detection techniques","volume":"1","author":"Bosamiya","year":"2013","journal-title":"Int. J. Recent Innov. Trends Comput. Commun."},{"key":"ref_8","first-page":"812","article-title":"A Survey on Disease and Nutrient Deficiency Detection in Cotton Plant","volume":"1","author":"Bosamiya","year":"2013","journal-title":"Int. J. Recent Innov. Trends Comput. Commun."},{"key":"ref_9","first-page":"26","article-title":"Animal detection using template matching algorithm","volume":"1","author":"Parikh","year":"2013","journal-title":"Int. J. Res. Mod. Eng. Emerg. Technol."},{"key":"ref_10","first-page":"14","article-title":"Intelligent Farm Surveillance System for Bird Detection","volume":"1","author":"Bhatt","year":"2012","journal-title":"Glob. J. Eng. Des. Technol."},{"key":"ref_11","first-page":"247","article-title":"Review of Different Techniques for Ripe Fruit Detection","volume":"4","author":"Bhatt","year":"2016","journal-title":"Int. J. Eng. Dev. Res."},{"key":"ref_12","first-page":"168455","article-title":"CP-BDHCA: Blockchain-based Confidentiality-Privacy preserving Big Data scheme for healthcare clouds and applications","volume":"9","author":"Ghayvat","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Patel, C., and Thakkar, S. (2020, January 13\u201315). Iris Recognition Supported Best Gabor Filters and Deep Learning CNN Options. Proceedings of the 2020 International Conference on Industry 4.0 Technology (I4Tech), Pune, India.","DOI":"10.1109\/I4Tech48345.2020.9102681"},{"key":"ref_14","unstructured":"Bhagchandani, A., Bhatt, D., and Chopade, M. (2018, January 14\u201316). Various Big Data Techniques to Process and Analyse Neuroscience Data. Proceedings of the 2018 5th International Conference on \u201cComputing for Sustainable Global Development\u201d, New Delhi, India."},{"key":"ref_15","first-page":"21","article-title":"Tumor Detection using Normalized Cross Co-Relation","volume":"1","author":"Soni","year":"2013","journal-title":"Int. J. Res. Mod. Eng. Emerg. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Patel, R., Patel, P., and Patel, C.I. (2011, January 15\u201317). Goal Detection from Unsupervised Video Surveillance. Proceedings of the International Conference on Advances in Computing and Information Technology, Chennai, India.","DOI":"10.1007\/978-3-642-22555-0_9"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"401","DOI":"10.7763\/IJCEE.2013.V5.740","article-title":"Robust face recognition using distance matrice","volume":"5","author":"Patel","year":"2013","journal-title":"Int. J. Comput. Electric. Eng."},{"key":"ref_18","unstructured":"Bhatt, D., and Bhagchandani, A. (2020, January 9\u201310). Machine Learning Model for Predicting Social Media Influence on Sports. Proceedings of the Ires International Conference, Online."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mehta, M., Passi, K., Chatterjee, I., and Patel, R. (2021). Knowledge Modelling and Big Data Analytics in Healthcare: Advances and Applications, Taylor and Francis.","DOI":"10.1201\/9781003142751"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7299","DOI":"10.3390\/s20247299","article-title":"Histogram of Oriented Based Fusion of Features for Human Activity Recognition","volume":"20","author":"Labana","year":"2020","journal-title":"Sensors"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.compeleceng.2016.06.004","article-title":"Human action recognition using fusion of features for unconstrained video sequences","volume":"70","author":"Garg","year":"2018","journal-title":"Comput. Electric. Eng."},{"key":"ref_22","unstructured":"Zhang, X., Zheng, H.-T., Sun, J., and Ma, N. (2018, January 18\u201322). ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. Proceedings of the Computer Vision and Pattern Recognition, Salt Lake City, UT, USA. Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/arxiv.org\/abs\/1807.11164v1."},{"key":"ref_23","unstructured":"Rastegari, M., Shapiro, L., Hajishirzi, H., and Mehta, S. (2019). ESPNetv2: A Light-Weight, Power Efficient, and General Purpose Convolutional Neural Network. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mehta, S., Hajishirzi, H., and Rastegari, M. (2020). DiCENet: Dimension-wise Convolutions for Efficient Networks. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2020.3041871"},{"key":"ref_25","unstructured":"Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C., and Sandler, M. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA."},{"key":"ref_26","unstructured":"Restegari, M., Caspi, A., Shapiro, L., Hajishirzi, H., and Mehta, S. (2018). ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation. arXiv."},{"key":"ref_27","unstructured":"Zhang, X., Ren, S., Sun, J., and He, K. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv."},{"key":"ref_28","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_29","unstructured":"Feng, C., Zhuo, S., Zhang, X., Shen, L., Aleksic, M., and Sheng, T. (2018, January 25). A Quantization-Friendly Separable Convolution for MobileNets. Proceedings of the 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2), Williamsburg, VA, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1109\/TIFS.2019.2936913","article-title":"Depth-Wise Separable Convolutions and Multi-Level Pooling for an Efficient Spatial CNN-Based Steganalysis","volume":"15","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"104948","DOI":"10.1016\/j.compag.2019.104948","article-title":"Depthwise separable convolution architectures for plant disease classification","volume":"165","author":"Yin","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","unstructured":"Choi, Y., Choi, H., and Yoo, B. (2018, January 13\u201316). Fast Depthwise Separable Convolution for Embedded Systems. Proceedings of the International Conference on Neural Information Processing (ICONIP), Siem Reap, Cambodia."},{"key":"ref_33","unstructured":"Kaiser, L., Gomez, A.N., and Chollet, F. (2017). Depthwise Separable Convolutions for Neural Machine Translation. arXiv."},{"key":"ref_34","unstructured":"Tran, M.-K., Yeung, S.-K., and Hua, B.-S. (2018, January 18\u201323). Pointwise Convolutional Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bracewell, R. (2003). Two-Dimensional Convolution. Fourier Analysis and Imaging, Springer.","DOI":"10.1007\/978-1-4419-8963-5"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/j.promfg.2020.01.386","article-title":"A 3D Convolutional Neural Network for Volumetric Image Semantic Segmentation","volume":"39","author":"Wang","year":"2019","journal-title":"Procedia Manuf."},{"key":"ref_37","unstructured":"Zhu, M., Chen, B., Kalenichenko, D., Wang, W., and Howard, A.G. (2017). Mobilenets:Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_38","unstructured":"Shen, L., Sun, G., and Hu, J. (2018, January 18\u201323). Squeeze-and-Excitation Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA."},{"key":"ref_39","unstructured":"(2022, January 10). PASCAL VOC Dataset. Available online: https:\/\/linproxy.fan.workers.dev:443\/http\/host.robots.ox.ac.uk\/pascal\/VOC\/voc2012\/."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gosling, J.B. (1980). Floating Point Operation. Design of Arithmetic Units for Digital Computers, Springer.","DOI":"10.1007\/978-1-349-16397-7"},{"key":"ref_41","unstructured":"(2022, January 20). MS COCO Dataset. Available online: https:\/\/linproxy.fan.workers.dev:443\/https\/cocodataset.org\/#download."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/22\/5\/1780\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:26:40Z","timestamp":1760135200000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/22\/5\/1780"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,24]]},"references-count":41,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22051780"],"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/s22051780","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,24]]}}}