Improved and Efficient Object Detection Algorithm based on YOLOv5

Authors

  • Amjad A. Alsuwaylimi Department of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Saudi Arabia
  • Rakan Alanazi Department of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Saudi Arabia
  • Sultan Munadi Alanazi Department of Computer Science, Science College, Northern Border University, Saudi Arabia
  • Sami Mohammed Alenezi Department of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Saudi Arabia
  • Taoufik Saidani Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Saudi Arabia
  • Refka Ghodhbani Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Saudi Arabia
Volume: 14 | Issue: 3 | Pages: 14380-14386 | June 2024 | https://doi.org/10.48084/etasr.7386

Abstract

Object detection is a fundamental and impactful area of exploration in computer vision and video processing, with wide-ranging applications across diverse domains. The advent of the You Only Look Once (YOLO) paradigm has revolutionized real-time object identification, particularly with the introduction of the YOLOv5 architecture. Specifically designed for efficient object detection, YOLOv5 has enhanced flexibility and computational efficiency. This study systematically investigates the application of YOLOv5 in object identification, offering a comprehensive analysis of its implementation. The current study critically evaluates the architectural improvements and additional functionalities of YOLOv5 compared to its previous versions, aiming to highlight its unique advantages. Additionally, it comprehensively evaluates the training process, transfer learning techniques, and other factors, advocating the integration of these features to significantly enhance YOLOv5's detection capabilities. According to the results of this study, YOLOv5 is deemed an indispensable technique in computer vision, playing a key role in achieving accurate object recognition. The experimental data showed that YOLOv5-tiny performed better than anticipated, with a mean Average Precision (mAP) of 60.9% when evaluated using an Intersection Over Union (IoU) criterion of 0.5. Compared to other approaches, the proposed framework is distinguished by significant improvements in the mean average accuracy, computational flexibility, and dependability. As a result, YOLOv5 is suitable for a wide range of real-world applications, since it is both sophisticated and resilient in addressing present issues in the fields of computer vision and video processing.

Keywords:

deep learning, object detection, YOLOv5, computer vision

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.

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.

Z. Ren, H. Zhang, and Z. Li, "Improved YOLOv5 Network for Real-Time Object Detection in Vehicle-Mounted Camera Capture Scenarios," Sensors, vol. 23, no. 10, 2023.

A. Balmik, S. Barik, and A. Nandy, "A Robust Object Recognition Using Modified YOLOv5 Neural Network," in 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, Mar. 2023, pp. 462–467.

T. Saidani, "Deep Learning Approach: YOLOv5-based Custom Object Detection," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12158–12163, Dec. 2023.

S. Sundaralingam and N. Ramanathan, "A Deep Learning-Based approach to Segregate Solid Waste Generated in Residential Areas," Engineering, Technology & Applied Science Research, vol. 13, no. 2, pp. 10439–10446, Apr. 2023.

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, 2021.

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.

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, 2023, Art. no. 312.

A. Balmik, S. Barik, and A. Nandy, "A Robust Object Recognition Using Modified YOLOv5 Neural Network," in 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, Mar. 2023, pp. 462–467.

S. Wu et al., "Enhanced YOLOv5 Object Detection Algorithm for Accurate Detection of Adult Rhynchophorus ferrugineus," Insects, vol. 14, no. 8, 2023.

N. Zendehdel, H. Chen, and M. C. Leu, "Real-time tool detection in smart manufacturing using You-Only-Look-Once (YOLO)v5," Manufacturing Letters, vol. 35, pp. 1052–1059, Aug. 2023.

J. Zhang, Z. Chen, G. Yan, Y. Wang, and B. Hu, "Faster and Lightweight: An Improved YOLOv5 Object Detector for Remote Sensing Images," Remote Sensing, vol. 15, no. 20, Jan. 2023, Art. no. 4974.

J. Solawetz, "Vehicles-OpenImages Dataset." 2020, [Online]. Available: 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.

R. Alharbey, A. Banjar, Y. Said, M. Atri, and M. Abid, "A Human Face Detector for Big Data Analysis of Pilgrim Flow Rates in Hajj and Umrah," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12861–12868, Feb. 2024.

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.

Downloads

How to Cite

[1]
A. A. Alsuwaylimi, R. Alanazi, S. M. Alanazi, S. M. Alenezi, T. Saidani, and R. Ghodhbani, “Improved and Efficient Object Detection Algorithm based on YOLOv5”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14380–14386, Jun. 2024.

Metrics

Abstract Views: 160
PDF Downloads: 83

Metrics Information

Most read articles by the same author(s)