References
[1]. A.de la Escalera et.al.,”Traffic sign recognition and analysis for intelligent vehicles”Volume 21, Issue 3,Volume 21, Issue 3,2003
[2]. I Barabás et al.,”Current challenges in autonomous driving”,IOP Publishing Ltd,2017
[3]. Xiaozhi Chen et al. ,”Monocular 3D Object Detection for Autonomous Driving”,Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2147-2156,2016
[4]. Zhong-Qiu Zhao et al.,”Object Detection With Deep Learning: A Review”, IEEE Transactions on Neural Networks and Learning Systems (Volume: 30, Issue: 11, 2019
[5]. Navneet Dalal et al. “Histograms of oriented gradients for human detection”
[6]. P Felzenszwalb et al.” A discriminatively trained, multiscale, deformable part model”
[7]. Joseph Redmon et al.” You Only Look Once:Unified, Real-Time Object Detection”
[8]. Ross Girshick et al. “Fast R-CNN”
[9]. David G. Lowe ”Object Recognition from Local Scale-Invariant Features”
[10]. Lowe, David G. “Distinctive image features from scale-invariant key points. ”
[11]. Sébastien Roy , Ingemar J. Cox “A Maximum-Flow Formulation of the N-camera Stereo Correspondence Problem”
[12]. MichaelBleyer , Margrit Gelautz “Graph-cut-based stereo matching using image segmentation with symmetrical treatment of occlusions”
[13]. Navneet Dalal , Bill Triggs “Histograms of Oriented Gradients for Human Detection”
[14]. Joseph Redmon et al.” YOLO9000: Better, Faster, Stronger”
[15]. Joseph Redmon et al.” YOLOv3: An Incremental Improvement”
[16]. Alexey Bochkovskiy et al.” YOLOv4: Optimal Speed and Accuracy of Object Detection”
[17]. Ross Girshick et al. “Rich feature hierarchies for accurate object detection and semantic segmentation Tech report (v5)”
[18]. Shaoqing Ren et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”
[19]. Jeong-ah Kim et al. “Comparison of Faster-RCNN, YOLO, and SSD for Real-Time Vehicle Type Recognition”
[20]. Priya Dwivedi. “YOLOv5 compared to Faster RCNN. Who wins?”
Cite this article
Hou,B. (2023). Theoretical analysis of the network structure of two mainstream object detection methods: YOLO and Fast RCNN. Applied and Computational Engineering,17,213-225.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 5th International Conference on Computing and Data Science
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).
References
[1]. A.de la Escalera et.al.,”Traffic sign recognition and analysis for intelligent vehicles”Volume 21, Issue 3,Volume 21, Issue 3,2003
[2]. I Barabás et al.,”Current challenges in autonomous driving”,IOP Publishing Ltd,2017
[3]. Xiaozhi Chen et al. ,”Monocular 3D Object Detection for Autonomous Driving”,Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2147-2156,2016
[4]. Zhong-Qiu Zhao et al.,”Object Detection With Deep Learning: A Review”, IEEE Transactions on Neural Networks and Learning Systems (Volume: 30, Issue: 11, 2019
[5]. Navneet Dalal et al. “Histograms of oriented gradients for human detection”
[6]. P Felzenszwalb et al.” A discriminatively trained, multiscale, deformable part model”
[7]. Joseph Redmon et al.” You Only Look Once:Unified, Real-Time Object Detection”
[8]. Ross Girshick et al. “Fast R-CNN”
[9]. David G. Lowe ”Object Recognition from Local Scale-Invariant Features”
[10]. Lowe, David G. “Distinctive image features from scale-invariant key points. ”
[11]. Sébastien Roy , Ingemar J. Cox “A Maximum-Flow Formulation of the N-camera Stereo Correspondence Problem”
[12]. MichaelBleyer , Margrit Gelautz “Graph-cut-based stereo matching using image segmentation with symmetrical treatment of occlusions”
[13]. Navneet Dalal , Bill Triggs “Histograms of Oriented Gradients for Human Detection”
[14]. Joseph Redmon et al.” YOLO9000: Better, Faster, Stronger”
[15]. Joseph Redmon et al.” YOLOv3: An Incremental Improvement”
[16]. Alexey Bochkovskiy et al.” YOLOv4: Optimal Speed and Accuracy of Object Detection”
[17]. Ross Girshick et al. “Rich feature hierarchies for accurate object detection and semantic segmentation Tech report (v5)”
[18]. Shaoqing Ren et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”
[19]. Jeong-ah Kim et al. “Comparison of Faster-RCNN, YOLO, and SSD for Real-Time Vehicle Type Recognition”
[20]. Priya Dwivedi. “YOLOv5 compared to Faster RCNN. Who wins?”