Overcoming sensor limits: How data fusion helps machines understand the world

In the real world, multiple types of modal information originate from the external environment and interrelate to form a whole. Multi-modal data fusion technology integrates data from diverse sources or types, enabling the generation of more comprehensive, accurate, and reliable information or decision-making than any single modality alone. In a survey published in the journal Computers, Materials & Continua, a team of researchers from China provided a comprehensive overview of deep learning-based methods, techniques, and applications for multi-modal fusion.

"Just as humans simultaneously use their eyes, ears, and sense of touch to understand the world, multimodal fusion technology endows machines with comprehensive perceptual capabilities." explains Tianzhe Jiao, one of the study’s authors. “Multi-modal data fusion can effectively overcome the limitations of single-modal data, such as noise, occlusion, and insufficient information, and has shown remarkable promise in fields like autonomous driving, smart healthcare, and sentiment analysis. Research shows that vehicles integrating both LiDAR and camera data achieve significantly higher target detection accuracy compared to systems using only a single sensor. Especially under harsh conditions such as rain, fog, or nighttime, the synergy of multi-source information can effectively eliminate blind spots.”

Despite its great potential, the research team also pointed out the challenges in real-world deployment: “Coordinating different information sources is like tuning a symphony orchestra—it requires addressing complex problems such as temporal synchronization and information complementarity,” the scientists note. Achieving more accurate and effective integration of multi-modal data to attain higher prediction precision remains a central issue across domains. While recent studies have proven the benefits of multi-modal fusion in various applications, fast and efficient multi-modal detection in real-world and complex environments is still challenging. Due to data heterogeneity and quality issues, further research is needed for many multi-modal tasks.

Multi-modal data fusion technology is breaking through the perception limitations of traditional AI by integrating visual, auditory, textual, and other sources to endow machines with a human-like, comprehensive cognitive ability. As Professor Jie Song from Northeastern University, the lead of this study, emphasizes: “This technology fundamentally enhances machine perception, compensating for the limitations of each modality by cross-verifying multiple sources of information.” He adds: “We have reviewed the research progress of multi-modal fusion technologies across different fields, focusing on guiding researchers to identify suitable fusion methods for various scenarios and providing constructive recommendations for the future development of multi-modal research.”