AI-based object detection latest trends in remote sensing, multimedia and agriculture applications
Object detection is a vital research direction in machine vision and deep learning. The object detection technique based on deep understanding has achieved tremendous progress in feature extraction, image representation, classification, and recognition in recent years, due to this rapid growth of de...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.1041514/full |
| _version_ | 1797852474247217152 |
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| author | Saqib Ali Nawaz Saqib Ali Nawaz Jingbing Li Jingbing Li Uzair Aslam Bhatti Uzair Aslam Bhatti Muhammad Usman Shoukat Raza Muhammad Ahmad |
| author_facet | Saqib Ali Nawaz Saqib Ali Nawaz Jingbing Li Jingbing Li Uzair Aslam Bhatti Uzair Aslam Bhatti Muhammad Usman Shoukat Raza Muhammad Ahmad |
| author_sort | Saqib Ali Nawaz |
| collection | DOAJ |
| description | Object detection is a vital research direction in machine vision and deep learning. The object detection technique based on deep understanding has achieved tremendous progress in feature extraction, image representation, classification, and recognition in recent years, due to this rapid growth of deep learning theory and technology. Scholars have proposed a series of methods for the object detection algorithm as well as improvements in data processing, network structure, loss function, and so on. In this paper, we introduce the characteristics of standard datasets and critical parameters of performance index evaluation, as well as the network structure and implementation methods of two-stage, single-stage, and other improved algorithms that are compared and analyzed. The latest improvement ideas of typical object detection algorithms based on deep learning are discussed and reached, from data enhancement, a priori box selection, network model construction, prediction box selection, and loss calculation. Finally, combined with the existing challenges, the future research direction of typical object detection algorithms is surveyed. |
| first_indexed | 2024-04-09T19:34:54Z |
| format | Article |
| id | doaj.art-c39831c130934e8b994c54710813e2ad |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-09T19:34:54Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-c39831c130934e8b994c54710813e2ad2023-04-04T16:23:49ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-11-011310.3389/fpls.2022.10415141041514AI-based object detection latest trends in remote sensing, multimedia and agriculture applicationsSaqib Ali Nawaz0Saqib Ali Nawaz1Jingbing Li2Jingbing Li3Uzair Aslam Bhatti4Uzair Aslam Bhatti5Muhammad Usman Shoukat6Raza Muhammad Ahmad7School of Information and Communication Engineering, Hainan University, Haikou, ChinaState Key Laboratory of Marine Resource Utilization in the South China Sea, Hainan University, Haikou, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou, ChinaState Key Laboratory of Marine Resource Utilization in the South China Sea, Hainan University, Haikou, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou, ChinaState Key Laboratory of Marine Resource Utilization in the South China Sea, Hainan University, Haikou, ChinaSchool of Automotive Engineering, Wuhan University of Technology, Wuhan, ChinaCollege of Cyberspace Security, Hainan University, Haikou, ChinaObject detection is a vital research direction in machine vision and deep learning. The object detection technique based on deep understanding has achieved tremendous progress in feature extraction, image representation, classification, and recognition in recent years, due to this rapid growth of deep learning theory and technology. Scholars have proposed a series of methods for the object detection algorithm as well as improvements in data processing, network structure, loss function, and so on. In this paper, we introduce the characteristics of standard datasets and critical parameters of performance index evaluation, as well as the network structure and implementation methods of two-stage, single-stage, and other improved algorithms that are compared and analyzed. The latest improvement ideas of typical object detection algorithms based on deep learning are discussed and reached, from data enhancement, a priori box selection, network model construction, prediction box selection, and loss calculation. Finally, combined with the existing challenges, the future research direction of typical object detection algorithms is surveyed.https://www.frontiersin.org/articles/10.3389/fpls.2022.1041514/fulldeep learningobject detectiontransfer learningalgorithm improvementdata augmentationnetwork structure |
| spellingShingle | Saqib Ali Nawaz Saqib Ali Nawaz Jingbing Li Jingbing Li Uzair Aslam Bhatti Uzair Aslam Bhatti Muhammad Usman Shoukat Raza Muhammad Ahmad AI-based object detection latest trends in remote sensing, multimedia and agriculture applications Frontiers in Plant Science deep learning object detection transfer learning algorithm improvement data augmentation network structure |
| title | AI-based object detection latest trends in remote sensing, multimedia and agriculture applications |
| title_full | AI-based object detection latest trends in remote sensing, multimedia and agriculture applications |
| title_fullStr | AI-based object detection latest trends in remote sensing, multimedia and agriculture applications |
| title_full_unstemmed | AI-based object detection latest trends in remote sensing, multimedia and agriculture applications |
| title_short | AI-based object detection latest trends in remote sensing, multimedia and agriculture applications |
| title_sort | ai based object detection latest trends in remote sensing multimedia and agriculture applications |
| topic | deep learning object detection transfer learning algorithm improvement data augmentation network structure |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2022.1041514/full |
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