YOLOatr : Deep Learning Based Automatic Target Detection and Localization in Thermal Infrared Imagery

TL;DR

This paper introduces YOLOatr, a modified YOLOv5s-based deep learning model designed for real-time target detection and localization in thermal infrared imagery, addressing domain-specific challenges and achieving state-of-the-art accuracy.

Contribution

The paper presents YOLOatr, a novel adaptation of YOLOv5s with tailored modifications for thermal IR imagery, improving ATR performance in challenging defense scenarios.

Findings

Achieves up to 99.6% ATR accuracy on DSIAC MWIR dataset.

Outperforms existing models in thermal infrared target detection.

Effective in real-time detection under varied environmental conditions.

Abstract

Automatic Target Detection (ATD) and Recognition (ATR) from Thermal Infrared (TI) imagery in the defense and surveillance domain is a challenging computer vision (CV) task in comparison to the commercial autonomous vehicle perception domain. Limited datasets, peculiar domain-specific and TI modality-specific challenges, i.e., limited hardware, scale invariance issues due to greater distances, deliberate occlusion by tactical vehicles, lower sensor resolution and resultant lack of structural information in targets, effects of weather, temperature, and time of day variations, and varying target to clutter ratios all result in increased intra-class variability and higher inter-class similarity, making accurate real-time ATR a challenging CV task. Resultantly, contemporary state-of-the-art (SOTA) deep learning architectures underperform in the ATR domain. We propose a modified anchor-based single-stage detector, called YOLOatr, based on a modified YOLOv5s, with optimal modifications to the detection heads, feature fusion in the neck, and a custom augmentation profile. We evaluate the performance of our proposed model on a comprehensive DSIAC MWIR dataset for real-time ATR over both correlated and decorrelated testing protocols. The results demonstrate that our proposed model achieves state-of-the-art ATR performance of up to 99.6%.