LGA-RCNN: Loss-Guided Attention for Object Detection

TL;DR

LGA-RCNN introduces a loss-guided attention module that enhances object detection by focusing on key regions, improving performance under challenging conditions like camouflage and complex environments.

Contribution

The paper presents a novel loss-guided attention module integrated into RCNN for improved object detection accuracy in difficult scenarios.

Findings

Enhanced detection accuracy under challenging conditions

Effective fusion of local and global information

Superior performance on benchmark datasets

Abstract

Object detection is widely studied in computer vision filed. In recent years, certain representative deep learning based detection methods along with solid benchmarks are proposed, which boosts the development of related researchs. However, existing detection methods still suffer from undesirable performance under challenges such as camouflage, blur, inter-class similarity, intra-class variance and complex environment. To address this issue, we propose LGA-RCNN which utilizes a loss-guided attention (LGA) module to highlight representative region of objects. Then, those highlighted local information are fused with global information for precise classification and localization.