Towards Reflected Object Detection: A Benchmark

2024-07-08

arXiv.org

AI Summary

Reflective object detection remains an underexplored yet challenging problem in object detection, where existing methods suffer severe performance degradation (over 30% mAP drop) in reflective scenes—primarily due to the absence of a dedicated benchmark. To address this gap, we introduce RODD, the first large-scale reflective object detection benchmark, comprising 21,059 real-world images across 10 categories, with dual annotations: bounding boxes and reflection-specific attributes. We formally define the task for the first time and construct the dataset following the COCO format. Extensive evaluations across five mainstream frameworks—Faster R-CNN, Mask R-CNN, YOLOv5/v8, and DETR—reveal their fundamental failure under reflective interference. RODD fills critical gaps in both data resources and standardized evaluation for reflective-aware detection. The dataset and code are publicly released to foster community advancement in this emerging direction.

Technology Category

• Computer Vision: Object Detection & Categorization
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Object detection has greatly improved over the past decade thanks to advances in deep learning and large-scale datasets. However, detecting objects reflected in surfaces remains an underexplored area. Reflective surfaces are ubiquitous in daily life, appearing in homes, offices, public spaces, and natural environments. Accurate detection and interpretation of reflected objects are essential for various applications. This paper addresses this gap by introducing a extensive benchmark specifically designed for Reflected Object Detection. Our Reflected Object Detection Dataset (RODD) features a diverse collection of images showcasing reflected objects in various contexts, providing standard annotations for both real and reflected objects. This distinguishes it from traditional object detection benchmarks. RODD encompasses 10 categories and includes 21,059 images of real and reflected objects across different backgrounds, complete with standard bounding box annotations and the classification of objects as real or reflected. Additionally, we present baseline results by adapting five state-of-the-art object detection models to address this challenging task. Experimental results underscore the limitations of existing methods when applied to reflected object detection, highlighting the need for specialized approaches. By releasing RODD, we aim to support and advance future research on detecting reflected objects. Dataset and code are available at: https: //github.com/Tqybu-hans/RODD.

Problem

• Detecting objects reflected in surfaces remains underexplored
• Need accurate detection of reflected objects for various applications
• Existing methods have limitations in reflected object detection

Innovation

• Introduces Reflected Object Detection Dataset (RODD)
• Adapts five state-of-the-art detection models
• Provides standard annotations for real and reflected objects