DoorDet: Semi-Automated Multi-Class Door Detection Dataset via Object Detection and Large Language Models
This work addresses a domain-specific problem for applications like building compliance checking and indoor scene understanding, but it is incremental as it builds on existing object detection and LLM methods.
The authors tackled the scarcity of fine-grained multi-class door detection datasets in floor plan drawings by developing a semi-automated pipeline using an object detector and a large language model, which significantly reduces annotation costs and produces a dataset suitable for benchmarking neural models.
Accurate detection and classification of diverse door types in floor plans drawings is critical for multiple applications, such as building compliance checking, and indoor scene understanding. Despite their importance, publicly available datasets specifically designed for fine-grained multi-class door detection remain scarce. In this work, we present a semi-automated pipeline that leverages a state-of-the-art object detector and a large language model (LLM) to construct a multi-class door detection dataset with minimal manual effort. Doors are first detected as a unified category using a deep object detection model. Next, an LLM classifies each detected instance based on its visual and contextual features. Finally, a human-in-the-loop stage ensures high-quality labels and bounding boxes. Our method significantly reduces annotation cost while producing a dataset suitable for benchmarking neural models in floor plan analysis. This work demonstrates the potential of combining deep learning and multimodal reasoning for efficient dataset construction in complex real-world domains.