Object detection: review and improvement based on deep learning

Research Article
Open access

Object detection: review and improvement based on deep learning

Tingyu Wang 1*
  • 1 Tianjin University    
  • *corresponding author tingyuwangx@gmail.com
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/13/20230701
ACE Vol.13
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-017-2
ISBN (Online): 978-1-83558-018-9

Abstract

In recent years, with the quantum leap in deep learning, self-driving vehicle as one of its applications has been gaining tremendously increasing popularity as well as making a multitude of achievements. Object detection, which has made significant contribution to driver-less vehicle, has had been applied to a tremendously wide range of fields. However, reports relevant to automatic vehicle stating that accidents are caused by Automatic driving technology, present problems pointing out that existing target detection algorithms, which are already fairly well reliable, can probably be interfered by adverse conditions such as high temperature, raise dust and transmission loss, and be not capable of providing precise output. This paper recaps on these previous classic algorithms, and their large-scale application domain. Meanwhile, this paper presents improvements focusing on enhancing the robustness of these algorithms to overcome these problems caused by adverse conditions and improve the accuracy. Thus these improvements could augment the security of these driver-less vehicles, and eventually reduce traffic accident mortality relative to self-driving vehicles and safeguard road safety, and may potentially benefit to further research.

Keywords:

target detection, deep learning, computer vision

Wang,T. (2023). Object detection: review and improvement based on deep learning. Applied and Computational Engineering,13,1-6.
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References

[1]. Jain A, Del Pero L, Grimmett H, and Ondruska P, Autonomy 2.0: Why is self-driving always 5 years away? 2021. DOI:10.48550/ARXIV.2107.08142.

[2]. He K. M, Gkioxari G, Dollár P, and Girshick R, Mask R-CNN. IEEE International Conference on Computer Vision (ICCV), Venice, 22-29 October 2017, pp.386-397.

[3]. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin Ma, Ghemawat S, Irving G, Isard M, et al. Tensorflow: a system for large-scale machine learning. In: 12th {USENIX} Symposium on operating systems design and implementation ({OSDI} 16), 2016; pp. 265–283.

[4]. KaiFu Lee, Yonggang Wang, Artificial Intelligence [M] Cultural Development Press, 2017.

[5]. Girshick R, Donahue J, Darrell T, and Malik J, Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 580-587, Doi: 10.1109/CVPR.2014.81.

[6]. Girshick R, “Fast R-CNN”. in 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 2015, pp.1440-1448.

[7]. Liu W, et al. SSD: Single Shot MultiBox Detector. In: Leibe B, Matas J, Sebe N, Welling M. (eds) Computer Vision -2016 European Conference on Computer Vision (ECCV), Amsterdam, 2016, pp.21-37.

[8]. Ge Z, Liu S, Wang F, Li Z, and Sun J, YOLOX: Exceeding YOLO Series in 2021, arXiv preprint arXiv:2017.08430, 2021.

[9]. Redmon J, Divvala S, Girshick R. and Farhadi A, You only look once: Unified real-time objectd etection, Proc. CVPR, 2016, pp. 779-788.

[10]. Pham V, and Dang T, Road Damage Detection and Classification with Detectron2 and Faster R-CNN, 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 5592-5601.

Cite this article

Wang,T. (2023). Object detection: review and improvement based on deep learning. Applied and Computational Engineering,13,1-6.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-017-2(Print) / 978-1-83558-018-9(Online)
Editor：Roman Bauer, Marwan Omar, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.13
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Jain A, Del Pero L, Grimmett H, and Ondruska P, Autonomy 2.0: Why is self-driving always 5 years away? 2021. DOI:10.48550/ARXIV.2107.08142.

[2]. He K. M, Gkioxari G, Dollár P, and Girshick R, Mask R-CNN. IEEE International Conference on Computer Vision (ICCV), Venice, 22-29 October 2017, pp.386-397.

[3]. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin Ma, Ghemawat S, Irving G, Isard M, et al. Tensorflow: a system for large-scale machine learning. In: 12th {USENIX} Symposium on operating systems design and implementation ({OSDI} 16), 2016; pp. 265–283.

[4]. KaiFu Lee, Yonggang Wang, Artificial Intelligence [M] Cultural Development Press, 2017.

[5]. Girshick R, Donahue J, Darrell T, and Malik J, Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 580-587, Doi: 10.1109/CVPR.2014.81.

[6]. Girshick R, “Fast R-CNN”. in 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 2015, pp.1440-1448.

[7]. Liu W, et al. SSD: Single Shot MultiBox Detector. In: Leibe B, Matas J, Sebe N, Welling M. (eds) Computer Vision -2016 European Conference on Computer Vision (ECCV), Amsterdam, 2016, pp.21-37.

[8]. Ge Z, Liu S, Wang F, Li Z, and Sun J, YOLOX: Exceeding YOLO Series in 2021, arXiv preprint arXiv:2017.08430, 2021.

[9]. Redmon J, Divvala S, Girshick R. and Farhadi A, You only look once: Unified real-time objectd etection, Proc. CVPR, 2016, pp. 779-788.

[10]. Pham V, and Dang T, Road Damage Detection and Classification with Detectron2 and Faster R-CNN, 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 5592-5601.

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