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To further enhance the detection accuracy for obscured vehicles and minimize the wrong detection rate, we integrate Kalman filter trackers into the detection algorithm. These trackers identify the vehicles and predict their future locations, effectively reducing both false positive and false negative detections. The resulting algorithm is lightweight and capable of producing highly accurate inference results in near real-time on a live-stream of LiDAR data. To demonstrate the applicability of our approach on small, unmanned vehicles/drones, we deploy the application on NVIDIA's Jetson Orin Nano embedded processor for AI. By optimizing the code using TensorRT for real-time performance, we achieve object detection and classification of flash LiDAR data at an average precision exceeding 95% and a rate of 60 frames-per-second. MATLAB plays a crucial role in enabling rapid prototyping and algorithm testing, facilitating the smooth transfer and deployment of the complex deep learning logic to an edge device without compromising performance or accuracy. |
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Object detection
Education and training
LIDAR
Video
Data modeling
Detection and tracking algorithms
Point clouds
