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International Journal of New Developments in Engineering and Society, 2024, 8(2); doi: 10.25236/IJNDES.2024.080213.

Comparative Study and Performance Optimization of Object Detection Algorithms

Author(s)

Yaxu Zhang, Shean Huang

Corresponding Author:
Yaxu Zhang
Affiliation(s)

School of Intelligent Engineering, Huanghe Jiaotong University, Jiaozuo, 454900, China

Abstract

This paper aims to compare and analyze the currently popular object detection algorithms and discuss performance optimization strategies for these algorithms. By considering the detection speed, accuracy, and robustness of the algorithms, this paper proposes several optimization methods aimed at improving the effectiveness of object detection in various practical application scenarios.

Keywords

Object Detection; Performance Optimization; Algorithm Comparison; Deep Learning; Real-time Processing

Cite This Paper

Yaxu Zhang, Shean Huang. Comparative Study and Performance Optimization of Object Detection Algorithms. International Journal of New Developments in Engineering and Society (2024) Vol.8, Issue 2: 85-91. https://doi.org/10.25236/IJNDES.2024.080213.

References

[1] Wang Guochao. Target Detection Algorithm [J]. Industrial Control Computer. 2023, 36(12): 10-11+14.

[2] He Nailei. Research on Forest Fire Image Recognition Based on Deep Learning Multi-Target Detection Technology [J]. Journal of Nanjing Forestry University (Natural Sciences Edition). 2023(12):13.

[3] Ma Sugang. Target Detection Algorithm with Adaptive Feature Fusion and cosIoU-NMS [J]. Journal of Computer-Aided Design and Computer Graphics. 2024(01):11.

[4] Li Changgao. Performance Optimization of Mobile Vision Target Detection Based on Robust MPC [J]. Computer Simulation. 2022, 39(05):191-195+200.