RAWDet-7: A Multi-Scenario Benchmark for Object Detection and Description on Quantized RAW Images

2026-02-03

AI Summary

This work addresses the limitation of current vision models that predominantly rely on ISP-processed RGB images, thereby discarding sensor-native information beneficial for machine reasoning and hindering efficient object detection and description under low-bit quantization. To bridge this gap, we present RAWDet-7, the first systematic benchmark for joint object detection and description on low-bit quantized RAW images. RAWDet-7 comprises approximately 25k training and 7.6k test images across seven object categories, offering diverse real-world RAW data captured across varying cameras, lighting conditions, and scenes, along with aligned object-level semantic descriptions. The benchmark supports 4/6/8-bit quantization studies and adheres to MS-COCO and LVIS annotation standards, providing a standardized platform to evaluate detection accuracy, description quality, detail preservation, and generalization—thereby advancing the effective utilization of raw sensor data in machine vision.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Most vision models are trained on RGB images processed through ISP pipelines optimized for human perception, which can discard sensor-level information useful for machine reasoning. RAW images preserve unprocessed scene data, enabling models to leverage richer cues for both object detection and object description, capturing fine-grained details, spatial relationships, and contextual information often lost in processed images. To support research in this domain, we introduce RAWDet-7, a large-scale dataset of ~25k training and 7.6k test RAW images collected across diverse cameras, lighting conditions, and environments, densely annotated for seven object categories following MS-COCO and LVIS conventions. In addition, we provide object-level descriptions derived from the corresponding high-resolution sRGB images, facilitating the study of object-level information preservation under RAW image processing and low-bit quantization. The dataset allows evaluation under simulated 4-bit, 6-bit, and 8-bit quantization, reflecting realistic sensor constraints, and provides a benchmark for studying detection performance, description quality&detail, and generalization in low-bit RAW image processing. Dataset&code upon acceptance.

Problem

• RAW images
• object detection
• object description
• low-bit quantization
• sensor-level information

Innovation

• RAW image
• low-bit quantization
• object detection
• object description
• multi-scenario benchmark