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Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5
Jiale Yao1, Xiangsuo Fan1,2, Bing Li3
1College of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China.
Sensors (Basel, Switzerland)
|November 11, 2022
Summary
This study enhances autonomous vehicle perception in adverse weather using an improved YOLOv5 model with adaptive image correction. The optimized algorithm significantly boosts target detection rates for safer self-driving operations.
Area of Science:
- Artificial Intelligence
- Computer Vision
- Autonomous Systems
Background:
- Autonomous vehicles face challenges in adverse weather conditions.
- Current detection systems struggle with reduced visibility and target clarity in harsh environments.
- Improving perception is crucial for autonomous driving safety.
Purpose of the Study:
- To enhance the target detection capabilities of autonomous vehicles in adverse weather.
- To develop an adaptive image correction model for improved clarity.
- To optimize the YOLOv5 algorithm for robust performance in challenging conditions.
Main Methods:
- A novel color levels offset compensation model was developed for adaptive image correction.
- Several one-stage object detection algorithms were evaluated.
- The YOLOv5 algorithm was enhanced by optimizing its Backbone parameters, incorporating Transformer and CBAM modules, and using the EIOU loss function.
Main Results:
- The proposed color correction model effectively improved target clarity in adverse weather.
- The enhanced YOLOv5 algorithm demonstrated improved target detection rates.
- The optimized model achieved a mean Average Precision (mAP) of 94.7% and a Frames Per Second (FPS) of 199.86.
Conclusions:
- The developed adaptive image correction and optimized YOLOv5 algorithm significantly improve autonomous vehicle detection in adverse weather.
- This approach offers a promising solution for enhancing the safety and reliability of self-driving cars in challenging environmental conditions.
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