Removal then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object Detection

TL;DR

This paper introduces a novel coarse-to-fine fusion approach for RGB-Infrared object detection, employing redundant spectrum removal and dynamic feature selection to enhance detection accuracy across lighting conditions.

Contribution

It proposes a new coarse-to-fine fusion framework with modules for removing redundant information and selecting features, improving multimodal object detection performance.

Findings

Outperforms existing methods on three datasets

Effective in daytime and nighttime conditions

Reduces fusion imprecision and redundant features

Abstract

In recent years, object detection utilizing both visible (RGB) and thermal infrared (IR) imagery has garnered extensive attention and has been widely implemented across a diverse array of fields. By leveraging the complementary properties between RGB and IR images, the object detection task can achieve reliable and robust object localization across a variety of lighting conditions, from daytime to nighttime environments. Most existing multi-modal object detection methods directly input the RGB and IR images into deep neural networks, resulting in inferior detection performance. We believe that this issue arises not only from the challenges associated with effectively integrating multimodal information but also from the presence of redundant features in both the RGB and IR modalities. The redundant information of each modality will exacerbates the fusion imprecision problems during propagation. To address this issue, we draw inspiration from the human brain's mechanism for processing multimodal information and propose a novel coarse-to-fine perspective to purify and fuse features from both modalities. Specifically, following this perspective, we design a Redundant Spectrum Removal module to remove interfering information within each modality coarsely and a Dynamic Feature Selection module to finely select the desired features for feature fusion. To verify the effectiveness of the coarse-to-fine fusion strategy, we construct a new object detector called the Removal then Selection Detector (RSDet). Extensive experiments on three RGB-IR object detection datasets verify the superior performance of our method.