Optimization of Unmanned Driving Obstacle Detection Method

Authors

  • Ziyan Li
  • Xinyi Liu

DOI:

https://doi.org/10.54097/1akyy485

Keywords:

Unmanned driving; Obstacle detection; Faster-RCNN model.

Abstract

In order to effectively enhance the accuracy of obstacle detection in unmanned driving on roads, this paper proposes an improved Faster-RCNN object detection model. Diverging from conventional Faster-RCNN models that replace the feature extraction network with ResNet50 instead of VGG16 and deepen the convolutional layers, allowing for a more comprehensive utilization of feature information. The proposed model is trained and tested in comparison with the EfficientNet network on the same dataset, VOC2007. Experimental results indicate that the proposed model exhibits higher precision in detecting obstacles on roads, showcasing broad applicability and achieving effective target recognition.

References

Zhao, X.Research on Path Optimization of Unmanned Agricultural Vehicles Based on Convolutional Neural Network. Agricultural Mechanization Research, 2024,46(07), 257-261.

Li, X., Li, X., & Li, L.Development and Application of Environmental Information Processing in Autonomous Driving. Automation Review, 2023,40(12), 26-30.

Jiao, Y., Su, C., & Huang, S.Research on Active Obstacle Detection System for Rail Transit Vehicles. Smart Rail Transit, 2023,60(06), 12-15.

Yang, T., Guo, Y., Wang, S., & Ma, X.Obstacle Recognition of Unmanned Driving Trolley Locomotives in Underground Coal Mines. Journal of Zhejiang University (Engineering Science), 2024,58(01), 29-39.

Hu, J. (2023). Simulation and Performance Analysis of Millimeter-Wave Radar under Unmanned Driving Conditions. Smart Rail Transit, 60(05), 6-11.

Wu, H., & Cheng, Q.Research on Steering and Braking Obstacle Avoidance Control Strategy for Unmanned New Energy Vehicles. Automotive Testing Report, 2023,(12), 76-78.

Yang, R.Research on Key Technologies of Unmanned Driving Based on ROS System. Dissertation, Xi'an University of Architecture and Technology,2023.

Chen, Q.Research on Path Tracking Control of Unmanned Vehicles with Local Planning. Dissertation, Northeast Petroleum University,2023.

Shen, B.Trajectory Planning and Obstacle Avoidance Design for Unmanned Vehicles Based on Model Predictive Control. Dissertation, North China University of Technology,2023.

Tong, J.Research on Obstacle Detection Technology for Mining Electric Locomotives Based on Improved Instance Segmentation. Dissertation, Anhui University of Science and Technology,2023.

Downloads

Published

08-05-2024

Issue

Section

Articles

How to Cite

Li, Z., & Liu, X. (2024). Optimization of Unmanned Driving Obstacle Detection Method. Mathematical Modeling and Algorithm Application, 2(1), 1-5. https://doi.org/10.54097/1akyy485