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Academic Journal of Computing & Information Science, 2023, 6(3); doi: 10.25236/AJCIS.2023.060307.

Robust Object Detection Model for Autonomous Driving in Real Scenarios


Ningchan Wang

Corresponding Author:
Ningchan Wang

Jurong Country Garden School, Jiangsu, 210007, China


This article explains the method of optimizing the object detection model used in the automatic drive. At present, there are a lot of car accidents happen caused by the low robustness of the automatic pilot. We are focused on improving the robustness of the model in a unique environment. We built four special environments and collected the images of the environment in a special way, and used them to train the detectron2, which is our initial model. Adversarial training is the way to train the model. Finally, we optimize the model successfully. If more people are willing to spend time and money training a particular model for a location, the automatic pilot can be promoted to any place.


Object detection, Adversarial training, Feature Pyramid Networks

Cite This Paper

Ningchan Wang. Robust Object Detection Model for Autonomous Driving in Real Scenarios. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 3: 52-61. https://doi.org/10.25236/AJCIS.2023.060307.


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