Drone capable of detecting abnormal flight patterns

Drone for Detecting Abnormal Driving Using Line Tracing and Deep Learning

This technology utilizes an onboard image acquisition unit and driving information acquisition unit to extract data on vehicle speed, acceleration, and steering angle. By applying line tracing and deep learning algorithms, it analyzes lane departure frequency and driving patterns to identify abnormal driving behavior.

Traditional, labor-intensive methods for enforcing traffic laws against drunk or abnormal driving suffer from low efficiency and structural limitations in providing real-time monitoring in hard-to-reach areas such as mountain roads or highways.

This technology captures vehicle driving footage via drone-mounted cameras, calculates driving metrics such as acceleration and angular velocity, and compares them against abnormal driving patterns learned through a logistic regression model before transmitting vehicle data to a control server. It offers a new approach to traffic enforcement and road safety management, enabling continuous monitoring of inaccessible areas without the need for manual intervention.

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Key Features:

• Image acquisition unit that captures driving footage of at least one vehicle on the road
• Driving information acquisition unit that extracts driving data from the captured footage
• Analysis unit that uses deep learning techniques to identify abnormal driving based on the acquired information
• System that recognizes lanes via line tracing techniques to perform individual vehicle assessments

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This invention was developed with support from the Ministry of Science, ICT and Future Planning for the development of a remote communication-based gas sensing analysis and judgment operation system utilizing machine learning.