Ghost-busting heat-assisted detection and ranging brings clarity to thermal imaging

It may not be too long before autonomous vehicles rule the roads. Despite a few widely reported mishaps, autonomous vehicles (AVs) are considered as safe or safer than human drivers in many respects.

They’re equipped with radar-detection and 360-degree cameras that are not impacted by a poor night’s sleep, a cellphone call or texting.

AVs have a built-in database of streets and highways, traffic lights, speed limits and various other pertinent details of the rules and regulations governing practically every foot of American roadways.

Still, AVs have their drawbacks. One of them was the focus of a recent study by researchers at Purdue University and Michigan State University.

AVs “see” the road through sonar, radar and LiDAR technology. LiDAR—Light Detection and Ranging—employs laser beams to determine the distance between two objects.

This visual apparatus does a largely admirable job for road navigation, but it has a key limitation.

As Zubin Jacob, a researcher at Purdue University, says, “Objects and their environment constantly emit and scatter thermal radiation, leading to textureless images famously known as the ‘ghosting effect.'”

Accurate detection of objects in real time is essential to guarantee accident-avoidance measures are taken in an instant. Auto maneuvers based on incorrect assessments drawn from blurry images, or ghosting, can make the difference between life and death.

Alternate means of improving detection have not been successful. High-resolution cameras, for instance, seemed promising but can falter when lighting is insufficient. Other multi-instrument approaches faced problems arising from data transmission interference.

But the Purdue and Michigan State researchers took an innovative approach they termed “Heat-assisted imaging and ranging.” The paper appeared in the journal Nature July 26.

With the application of machine learning algorithms and approaches called TeX decomposition and TeX vision, the researchers were able to eliminate barriers posed by darkness, fog and smoke and clearly capture images with infrared cameras.

Currently used modalities such as sonar, radar and LiDAR “send out signals and detect the reflection to infer the presence/absence of any object and its distance,” explained Jacob. “This gives extra information of the scene in addition to the camera vision, especially when the ambient illumination is poor.”

His team’s approach—heat-assisted detection and ranging (HADAR)—is “fundamentally different,” he said.

“It uses invisible infrared radiation to reconstruct a night-time scene with clarity like daytime.”

In an example offered by the researchers, they created an outdoor nighttime scene with a dark colored car, a human driver and a life-size cutout figure of Albert Einstein.

The results show LiDAR technology, despite its capacity for highly accurate surface measurements and 3D mapping, nevertheless could not distinguish the human from a cardboard cutout figure and struggled to discern the dark car at night. Optical cameras failed, too, because of poor perception at night.

HADAR, however, distinguished between people and the cardboard figure by identifying skin and fabric elements.

HADAR is not yet ready for use outside the lab. Real-time data-acquisition poses challenges and equipment costs currently are prohibitive.

But the researchers say it may not be long before HADAR is used not only for autonomous navigation, but in health care, agriculture and wildlife observation. It could also prove beneficial to firefighters and the military.

Jacob confessed to a personal motivation behind his efforts on the project.

“To be honest, I am afraid of the dark. Who isn’t?” he said. “It is great to know that thermal photons carry vibrant information in the night similar to daytime. Someday we will have machine perception using HADAR which is so accurate that it does not distinguish between night and day.”

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