Using Heat Sensors to Enable Robust Machine Perception

Saturday - August 19 2023, 15:08 UTC - 1 year ago

HADAR is a machine perception technology that combines thermal physics, infrared imaging and machine learning to enable smart devices to perceive objects in darkness and bad weather. HADAR TeX vision has the potential to increase the accuracy of autonomous vehicles and robots, as well as to be used in geosciences, health care and wildlife monitoring applications.

HADAR combines thermal physics, infrared imaging and machine learning to pave the way to fully passive and physics-aware machine perception. "Our work builds the information theoretic foundations of thermal perception to show that pitch darkness carries the same amount of information as broad daylight. Evolution has made human beings biased toward the daytime. Machine perception of the future will overcome this long-standing dichotomy between day and night," Jacob said. The team tested HADAR TeX vision using an off-road nighttime scene. "HADAR TeX vision recovered textures and overcame the ghosting effect," Bao said. "It recovered fine textures such as water ripples, bark wrinkles and culverts in addition to details about the grassy land."Additional improvements to HADAR are improving the size of the hardware and the data collection speed. "The current sensor is large and heavy since HADAR algorithms require many colors of invisible infrared radiation," Bao said. "To apply it to self-driving cars or robots, we need to bring down the size and price while also making the cameras faster. The current sensor takes around one second to create one image, but for autonomous cars we need around 30 to 60 hertz frame rate, or frames per second."HADAR TeX vision’s initial applications are automated vehicles and robots that interact with humans in complex environments. The technology could be further developed for agriculture, defense, geosciences, health care and wildlife monitoring applications. Traditional active sensors like LiDAR, or light detection and ranging, radar and sonar emit signals and subsequently receive them to collect 3D information about a scene. These methods have drawbacks that increase as they are scaled up, including signal interference and risks to people’s eye safety. In comparison, video cameras that work based on sunlight or other sources of illumination are advantageous, but low-light conditions such as nighttime, fog or rain present a serious impediment. Traditional thermal imaging is a fully passive sensing method that collects invisible heat radiation originating from all objects in a scene. It can sense through darkness, inclement weather and solar glare. But Jacob said fundamental challenges hinder its use today.

Machine perception uses advanced sensors to collect information about the surrounding scene for situational awareness. State-of-the-art machine perception using active sonar, radar and LiDAR to enhance camera vision9 faces difficulties when the number of intelligent agents scales up. Exploiting omnipresent heat signal could be a new frontier for scalable perception. However, objects and their environment constantly emit and scatter thermal radiation, leading to textureless images famously known as the ‘ghosting effect’. Thermal vision thus has no specificity limited by information loss, whereas thermal ranging—crucial for navigation—has been elusive even when combined with artificial intelligence (AI). Here we propose and experimentally demonstrate heat-assisted detection and ranging (HADAR) overcoming this open challenge of ghosting and benchmark it against AI-elevated traditional thermal imaging and deep — learning — powered passive ranging. HADAR is a fully passive heat – detecting sensor modality, enabling robust machine perception in night or day, rain or fog.