Synthetic imagery for fuzzy object detection: A comparative study

TL;DR

This study explores the use of synthetic fire images generated from 3D models for training fuzzy object detection models, demonstrating that mixed training with synthetic and real data enhances detection performance and reduces annotation costs.

Contribution

It introduces an automated method for generating and annotating synthetic fuzzy object images, improving training efficiency and model accuracy in fire detection tasks.

Findings

Synthetic data effectively improves fire detection accuracy.

Mixed training with synthetic and real images outperforms training on real or synthetic data alone.

Automated annotation reduces time and cost in dataset creation.

Abstract

The fuzzy object detection is a challenging field of research in computer vision (CV). Distinguishing between fuzzy and non-fuzzy object detection in CV is important. Fuzzy objects such as fire, smoke, mist, and steam present significantly greater complexities in terms of visual features, blurred edges, varying shapes, opacity, and volume compared to non-fuzzy objects such as trees and cars. Collection of a balanced and diverse dataset and accurate annotation is crucial to achieve better ML models for fuzzy objects, however, the task of collection and annotation is still highly manual. In this research, we propose and leverage an alternative method of generating and automatically annotating fully synthetic fire images based on 3D models for training an object detection model. Moreover, the performance, and efficiency of the trained ML models on synthetic images is compared with ML models trained on real imagery and mixed imagery. Findings proved the effectiveness of the synthetic data for fire detection, while the performance improves as the test dataset covers a broader spectrum of real fires. Our findings illustrates that when synthetic imagery and real imagery is utilized in a mixed training set the resulting ML model outperforms models trained on real imagery as well as models trained on synthetic imagery for detection of a broad spectrum of fires. The proposed method for automating the annotation of synthetic fuzzy objects imagery carries substantial implications for reducing both time and cost in creating computer vision models specifically tailored for detecting fuzzy objects.