Work

Comparing Human Performance on Target Localization in Near Infrared and Long Wave Infrared for Cluttered Environments

Year: 2024

Type: article

Abstract: In the context of rapid advancements in AI, the accuracies and speeds among various AI models and methods are often compared. However, a basic question is rarely asked: is AI better than humans, and if so, under what conditions? This paper investigates human ability to detect distant landmark targets under cluttered surroundings such as buildings, trees, and clouds in NIR and LWIR images, aiming to facilitate AI object detection performance analysis. Our investigation employs perception tests and a human performance model to analyze object detection capabilities. The results reveal distinctive differences in NIR and LWIR detectability, showing that although LWIR performs less effectively at range, it offers superior robustness across various environmental conditions. Our findings suggest that AI could be particularly advantageous for object detection in LWIR as it outperform humans in terms of detection accuracy at a long range.

Source: Sensors

DOI: 10.3390/s24206662

Authors: Li Zhang, Mark Martino, Orges Furxhi, Eddie L. Jacobs, Ronald G. Driggers, C. Kyle Renshaw

Institutions: University of Central Florida, University of Memphis, University of Arizona

Language: English

Topic: Infrared Target Detection Methodologies

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,955

Funder: Army Contracting Command - Aberdeen Proving Ground