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A new detection algorithm for alien intrusion on highway.

Junmei Guo¹, Haitong Lou¹, Haonan Chen¹

¹The School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Shandong, China.

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Summary

This summary is machine-generated.

This study introduces CS-YOLO, an object detection algorithm to reduce highway accidents caused by foreign objects. CS-YOLO improves accuracy and reduces computational load for better emergency response.

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Area of Science:

Computer Science
Artificial Intelligence
Road Safety Engineering

Background:

Highway accidents are frequent, often caused by unexpected foreign body intrusions.
Timely detection of foreign objects is crucial for preventing accidents and ensuring road safety.

Purpose of the Study:

To develop an effective object detection algorithm for identifying foreign bodies on highways.
To enhance the accuracy and efficiency of real-time intrusion detection systems.

Main Methods:

Proposed a novel feature extraction module to preserve critical information.
Introduced an improved feature fusion method to boost object detection accuracy.
Developed a lightweight computational approach to reduce complexity.

Main Results:

CS-YOLO demonstrated superior performance compared to existing algorithms.
Achieved 3.6% higher accuracy on the Visdrone dataset (small targets).
Showed 1.2% and 1.4% accuracy improvements on the Tinypersons and VOC2007 datasets, respectively.

Conclusions:

The proposed CS-YOLO algorithm effectively detects highway intrusions.
CS-YOLO offers a promising solution for enhancing road safety through advanced object detection.