Deep Learning Approach: YOLOv5-based Custom Object Detection

Authors

  • Taoufik Saidani Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia | Laboratory of Electronics and Microelectronics (EμE), Faculty of Sciences, Monastir University, Monastir, Tunisia
Volume: 13 | Issue: 6 | Pages: 12158-12163 | December 2023 | https://doi.org/10.48084/etasr.6397

Abstract

Object detection is of significant importance in the field of computer vision, since it has extensive applications across many sectors. The emergence of YOLO (You Only Look Once) has brought about substantial changes in this domain with the introduction of real-time object identification with exceptional accuracy. The YOLOv5 architecture is highly sought after because of its increased flexibility and computational efficiency. This research provides an in-depth analysis of implementing YOLOv5 for object identification. This research delves deeply into the architectural improvements and design ideas that set YOLOv5 apart from its predecessors to illuminate its unique benefits. This research examines the training process and the efficiency of transfer learning techniques, among other things. The detection skills of YOLOv5 may be greatly improved by including these features. This study suggests the use of YOLOv5, a state-of-the-art object identification framework, as a crucial tool in the field of computer vision for accurate object recognition. The results of the proposed framework demonstrate higher performance in terms of mAP (60.9%) when evaluated with an IoU criterion of 0.5 and when compared to current methodologies in terms of reliability, computing flexibility, and mean average precision. These advantages make it applicable in many real-world circumstances.

Keywords:

computer vision, object detection, deep learning, YOLOv5

Downloads

Download data is not yet available.

References

W. Wang, Q. Lai, H. Fu, J. Shen, H. Ling, and R. Yang, "Salient Object Detection in the Deep Learning Era: An In-Depth Survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 6, pp. 3239–3259, Jun. 2022. DOI: https://doi.org/10.1109/TPAMI.2021.3051099

I. Bilik, O. Longman, S. Villeval, and J. Tabrikian, "The Rise of Radar for Autonomous Vehicles: Signal Processing Solutions and Future Research Directions," IEEE Signal Processing Magazine, vol. 36, no. 5, pp. 20–31, Sep. 2019. DOI: https://doi.org/10.1109/MSP.2019.2926573

F. Guede-Fernández, L. Martins, R. V. de Almeida, H. Gamboa, and P. Vieira, "A Deep Learning Based Object Identification System for Forest Fire Detection," Fire, vol. 4, no. 4, Dec. 2021, Art. no. 75. DOI: https://doi.org/10.3390/fire4040075

Y. Zhang, Z. Guo, J. Wu, Y. Tian, H. Tang, and X. Guo, "Real-Time Vehicle Detection Based on Improved YOLO v5," Sustainability, vol. 14, no. 19, Jan. 2022, Art. no. 12274. DOI: https://doi.org/10.3390/su141912274

B. Mahaur and K. K. Mishra, "Small-object detection based on YOLOv5 in autonomous driving systems," Pattern Recognition Letters, vol. 168, pp. 115–122, Apr. 2023. DOI: https://doi.org/10.1016/j.patrec.2023.03.009

J. Solawetz, "Vehicles-OpenImages Dataset," Roboflow. https://public.roboflow.com/object-detection/vehicles-openimages.

R. Arifando, S. Eto, and C. Wada, "Improved YOLOv5-Based Lightweight Object Detection Algorithm for People with Visual Impairment to Detect Buses," Applied Sciences, vol. 13, no. 9, Jan. 2023, Art. no. 5802. DOI: https://doi.org/10.3390/app13095802

M. Salemdeeb and S. Erturk, "Multi-national and Multi-language License Plate Detection using Convolutional Neural Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 5979–5985, Aug. 2020. DOI: https://doi.org/10.48084/etasr.3573

S. Sahel, M. Alsahafi, M. Alghamdi, and T. Alsubait, "Logo Detection Using Deep Learning with Pretrained CNN Models," Engineering, Technology & Applied Science Research, vol. 11, no. 1, pp. 6724–6729, Feb. 2021. DOI: https://doi.org/10.48084/etasr.3919

D. D. Van, "Application of Advanced Deep Convolutional Neural Networks for the Recognition of Road Surface Anomalies," Engineering, Technology & Applied Science Research, vol. 13, no. 3, pp. 10765–10768, Jun. 2023. DOI: https://doi.org/10.48084/etasr.5890

M.-F. R. Lee and Y.-C. Chen, "Artificial Intelligence Based Object Detection and Tracking for a Small Underwater Robot," Processes, vol. 11, no. 2, Feb. 2023, Art. no. 312. DOI: https://doi.org/10.3390/pr11020312

J. Li et al., "Detection of Smoke from Straw Burning Using Sentinel-2 Satellite Data and an Improved YOLOv5s Algorithm," Remote Sensing, vol. 15, no. 10, Jan. 2023, Art. no. 2641. DOI: https://doi.org/10.3390/rs15102641

Downloads

How to Cite

[1]
Saidani, T. 2023. Deep Learning Approach: YOLOv5-based Custom Object Detection. Engineering, Technology & Applied Science Research. 13, 6 (Dec. 2023), 12158–12163. DOI:https://doi.org/10.48084/etasr.6397.

Metrics

Abstract Views: 921
PDF Downloads: 806

Metrics Information

Most read articles by the same author(s)