{ "ok": true, "doi": "10.46243/jst.2022.v7.i02.pp291-300", "doi_display": "10.46243/jst.2022.v7.i02.pp291-300", "doi_url": "https://doi.org/10.46243/jst.2022.v7.i02.pp291-300", "state": "registered", "url": "https://www.jst.org.in/index.php/pub/article/view/476", "title": "A COMPARATIVE STUDY OF VARIOUS OBJECT DETECTION AND FEATURE EXTRACTION ALGORITHMS", "version": 2, "registered_via": "crossref", "prefix": { "prefix": "10.46243", "status": "live" }, "registrant": { "name": "Longman Publishers", "kind": "publisher", "country": "India" }, "reserved_at": null, "registered_at": "2026-09-29 22:00:27", "updated_at": "2026-09-29 23:59:46", "withdrawn_at": null, "withdrawn_reason": null, "record": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2022.v7.i02.pp291-300", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "A COMPARATIVE STUDY OF VARIOUS OBJECT DETECTION AND FEATURE EXTRACTION ALGORITHMS", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2022.v7.i02.pp291-300" } ], "agents": [ { "role": "author", "name": { "given": "Santhosh S Nayak", "family": "Santhosh S Nayak" }, "sequence": "first" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2022-07-03", "date_type": "PublicationDate", "online": "2022-07-03" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "7", "issue": "2", "pages": { "first": "291", "last": "300" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/476", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/476/423", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/476/2192", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://jst.org.in/admin/uploads/JST070233.pdf", "purpose": "similarity-checking" } ], "abstract": { "value": "Agriculture is the most important sector in the Indian economy's development. In today's agriculture, there is a high demand to transition from tedious time-consuming manual harvesting to a fully automated operation. The combination of both autonomous harvesting and segregation of tomato fruit and three different phases or conditions of tomato is classified in this study. This project uses an image processing technique to classify the tomato's condition using a sample OpenCV tool. A Raspberry Pi camera is used to capture video continuously and this R-pi converts real time input video into frame, then object detection and image processing algorithm is performed on those image frame to detect shape of tomato, color filter is applied on tomato image to detect color of tomato. This paper suggests various important approaches for image processing and also suggests the most popular and widely used algorithms for object detection", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2022-07-03", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1007/s11694-021-01074-7", "unstructured": "Kuang-Wen Hsieh, Bo–Yu Huang,Kai-Ze Hsiao,Yu-Hao Tuan and Fu-Pang Shih"Fruit maturity and location identification of beef tomato using R-CNN and binocular imaging technology”Journal of Food Measurement and characterization 15, 5170-5180. doi:10.1007/s11694- 021-01074-7, 2021" }, { "key": "ref2", "doi": "10.1038/nature14539", "unstructured": "Y. LeCun, Y. Bengio, G. Hinton, “Deep Learning”, Nature, Vol.521, pp.436-444, 2015" }, { "key": "ref3", "doi": "10.1109/iccv.2015.169", "unstructured": "R. Girshick, “Fast R-CNN”, IEEE International Conference on Computer Vision (ICCV), Santiago, USA pp.1440-1448, 2015" }, { "key": "ref4", "doi": "10.1109/tnnls.2018.2876865", "unstructured": "Z. Zhao, X. Wu, S. Xu, P. Zheng, “Object Detection with Deep Learning: A Review”, IEEE Transactions on Neural Networksand Learning Systems, Vol.30, Issue.11, pp.3212-3232, 2019" }, { "key": "ref5", "unstructured": "J. Redmon, A. Farhadi, “YOLOv3: An Incremental Improvement”, ResearchGate, 2018" }, { "key": "ref6", "doi": "10.1109/tpami.2016.2577031", "unstructured": "S. Ren, K. He, R. Girshick, J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.39, Issue.6, pp.1137-1149, 2017" }, { "key": "ref7", "doi": "10.1007/978-3-319-46448-0_2", "unstructured": "W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu, A.C. Berg, “SSD: Single Shot Multibox Detector”, Springer, Vol.9905, pp.21-37, 2016" }, { "key": "ref8", "doi": "10.4236/jcc.2018.611009", "unstructured": "J. Y. Lu, C. Ma, L. Li, X.Y. Xing, Y. Zhang, Z.G. Wang. J.W. Xu, “A Vehicle Detection Method for Aerial Image Based on YOLO”, Journal of Computer and Communications, Vol.6, Issue.11, pp.98- 107, 2018" }, { "key": "ref9", "doi": "10.3390/electronics9030537", "unstructured": "L. Zhao, S. Li, “Object Detection Algorithm Based on Improved YOLOv3”, Electronics, Vol.9, Issue.3, pp.537, 2020" }, { "key": "ref10", "unstructured": "N. Raviteja, M. Lavanya, S. Sangeetha, “An Overview on Object Detection and Recognition”, International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.42-45, 2020" }, { "key": "ref11", "unstructured": "A. Kaur, D. Kaur, “Yolo Deep Learning Model Based Algorithm for Object Detection”, International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.174-178, 2020" }, { "key": "ref12", "doi": "10.3390/app10093079", "unstructured": "Yi-Qi Huang, Jia-Chun Zheng, Shi-Dan Sun, Cheng-Fu Yang, and Jing Liu 2 “Optimized YOLOv3 Algorithm and Its Application in Traffic Flow Detections”" }, { "key": "ref13", "doi": "10.35940/ijrte.b1154.0782s319", "unstructured": "K G Shreyas Dixit, Mahima Girish Chadaga, Sinchana S Savalgimath, G Ragavendra Rakshith, Naveen Kumar M R “Evaluation and Evolution of Object Detection Techniques YOLO and R-CNN” International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-2S3, July 2019" }, { "key": "ref14", "doi": "10.1109/cvpr.2014.81", "unstructured": "R. Girshick, J. Donahue, T. Darrell, J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and SemanticSegmentation”, IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, pp. 580-587, 2014" } ], "record": { "registrant": "Longman Publishers", "registered": "2024-02-16", "updated": "2026-09-17", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "record_sha256": "b787dfd506bc91e164c7afd37f44075bc61529fdc5f8bf19909217c007aa1a6f", "handle": { "synced_at": "2026-10-01 18:12:35", "url": "https://www.jst.org.in/index.php/pub/article/view/476" }, "links": { "record_page": "https://registry.smartscholars.in/record.php?doi=10.46243%2Fjst.2022.v7.i02.pp291-300", "system_metadata": "https://registry.smartscholars.in/resolve.php?doi=10.46243%2Fjst.2022.v7.i02.pp291-300&as=system", "history": "https://registry.smartscholars.in/api.php?action=history&doi=10.46243%2Fjst.2022.v7.i02.pp291-300", "kernel_xml": "https://registry.smartscholars.in/resolve.php?doi=10.46243%2Fjst.2022.v7.i02.pp291-300&as=xml" } }