A High-Performance Thermal Infrared Object Detection Framework with Centralized Regulation

TL;DR

This paper introduces CRT-YOLO, a novel thermal infrared object detection framework that employs centralized feature regulation and multi-scale attention modules to enhance detection accuracy by capturing long-range dependencies with minimal computational cost.

Contribution

The paper proposes a new thermal infrared object detection model, CRT-YOLO, featuring centralized feature regulation and efficient multi-scale attention modules for improved global feature interaction.

Findings

CRT-YOLO outperforms traditional methods on benchmark datasets.

The proposed modules effectively capture long-range dependencies.

Ablation studies confirm the modules' contribution to performance.

Abstract

Thermal Infrared (TIR) technology involves the use of sensors to detect and measure infrared radiation emitted by objects, and it is widely utilized across a broad spectrum of applications. The advancements in object detection methods utilizing TIR images have sparked significant research interest. However, most traditional methods lack the capability to effectively extract and fuse local-global information, which is crucial for TIR-domain feature attention. In this study, we present a novel and efficient thermal infrared object detection framework, known as CRT-YOLO, that is based on centralized feature regulation, enabling the establishment of global-range interaction on TIR information. Our proposed model integrates efficient multi-scale attention (EMA) modules, which adeptly capture long-range dependencies while incurring minimal computational overhead. Additionally, it leverages the Centralized Feature Pyramid (CFP) network, which offers global regulation of TIR features. Extensive experiments conducted on two benchmark datasets demonstrate that our CRT-YOLO model significantly outperforms conventional methods for TIR image object detection. Furthermore, the ablation study provides compelling evidence of the effectiveness of our proposed modules, reinforcing the potential impact of our approach on advancing the field of thermal infrared object detection.