Vehicle Object Detection Using Sensor-Guided Frame Extraction
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Solution Overview
Problem
Current autonomous driving systems face challenges in accurately and efficiently detecting and tracking objects around a vehicle in real-time, particularly in complex environments, due to limitations in sensor data processing and resource constraints.
Innovation Solution
An electronic device equipped with multiple cameras and sensors, such as RADAR and LIDAR, that captures and processes image frames at preset intervals, performs object detection using deep learning algorithms, and tracks object changes across multiple frames, efficiently distributing processing tasks between GPUs and CPUs to enhance accuracy and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object detection is performed on all captured image frames, then detection accuracy is improved, but processing time and resource consumption increase
Solution Approach 1:
The system performs preliminary object sensing using sensors (such as radar or lidar) before conducting detailed object detection on image frames. This preliminary action identifies potential objects of interest, allowing the system to focus subsequent image processing resources only on frames containing sensed objects, thereby maintaining high detection accuracy while significantly reducing overall processing time and computational load.
2Measurement precision
If multiple cameras and sensors are used to improve detection accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system divides the detection task into separate functional segments: sensors (such as radar or lidar) perform initial object sensing and location identification, while cameras capture and process visual image frames. This segmentation allows each component to specialize in its optimal function, improving overall detection accuracy while managing system complexity through modular architecture where each module handles a specific aspect of the detection process.
3Measurement precision
If object detection is performed frequently to improve tracking accuracy, then detection accuracy is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic object sensing at predetermined time intervals using sensors, rather than continuously processing all image frames. This periodic action maintains adequate tracking accuracy by regularly updating object positions and characteristics while significantly reducing energy consumption compared to continuous full-frame detection. The system adjusts the sensing interval based on object importance and motion characteristics to optimize the balance between tracking accuracy and energy usage.
Data Source
AI summary
An electronic device for and a method of assisting vehicle driving are provided. The electronic device includes a plurality of cameras configured to capture a surrounding image around a vehicle; at least one sensor configured to sense an object around the vehicle; and a processor configured to obtain, during vehicle driving, a plurality of image frames as the surrounding image of the vehicle is captured based on a preset time interval by using the plurality of cameras, based on the object is sensed using the at least one sensor while the vehicle is being driven, extract an image frame corresponding to a time point when and a location where the object has been sensed, from among the obtained plurality of image frames, perform object detection from the extracted image frame, and perform object tracking of tracking a change in the object, from a plurality of image frames obtained after the extracted image frame. The present disclosure also relates to an artificial intelligence (AI) system that utilizes a machine learning algorithm, such as deep learning, and applications of the AI system.