Fusion Detection

Fusion detection integrates data from multiple sensors (e.g., cameras, LiDAR, radar) to improve object detection accuracy and robustness, addressing limitations of individual sensors. Current research focuses on developing efficient fusion algorithms, including convolutional neural networks (CNNs), transformers, and spiking neural networks (SNNs), and addressing challenges like sensor miscalibration and asynchronous data streams. These advancements are crucial for applications such as autonomous driving, remote sensing, and active safety systems, enabling more reliable and accurate object detection in diverse and challenging environments. The development of robust and efficient fusion methods is driving improvements in the performance and reliability of these systems.