HOD: A Benchmark Dataset for Harmful Object Detection

TL;DR

This paper introduces HOD, a comprehensive benchmark dataset with over 10,000 images across six categories, aimed at advancing automatic harmful object detection in online media to improve content filtering and user safety.

Contribution

The paper presents a new large-scale, multi-category dataset for harmful object detection, addressing limitations of previous datasets and providing a valuable resource for developing real-time detection systems.

Findings

The dataset includes both normal and hard-to-detect harmful cases.

State-of-the-art object detection models perform effectively on this dataset.

The dataset is publicly available for research and development.

Abstract

Recent multi-media data such as images and videos have been rapidly spread out on various online services such as social network services (SNS). With the explosive growth of online media services, the number of image content that may harm users is also growing exponentially. Thus, most recent online platforms such as Facebook and Instagram have adopted content filtering systems to prevent the prevalence of harmful content and reduce the possible risk of adverse effects on users. Unfortunately, computer vision research on detecting harmful content has not yet attracted attention enough. Users of each platform still manually click the report button to recognize patterns of harmful content they dislike when exposed to harmful content. However, the problem with manual reporting is that users are already exposed to harmful content. To address these issues, our research goal in this work is to develop automatic harmful object detection systems for online services. We present a new benchmark dataset for harmful object detection. Unlike most related studies focusing on a small subset of object categories, our dataset addresses various categories. Specifically, our proposed dataset contains more than 10,000 images across 6 categories that might be harmful, consisting of not only normal cases but also hard cases that are difficult to detect. Moreover, we have conducted extensive experiments to evaluate the effectiveness of our proposed dataset. We have utilized the recently proposed state-of-the-art (SOTA) object detection architectures and demonstrated our proposed dataset can be greatly useful for the real-time harmful object detection task. The whole source codes and datasets are publicly accessible at https://github.com/poori-nuna/HOD-Benchmark-Dataset.