  Accurate real-time object detection is vital across numerous industrial
applications, from safety monitoring to quality control. Traditional
approaches, however, are hindered by arduous manual annotation and data
collection, struggling to adapt to ever-changing environments and novel target
objects. To address these limitations, this paper presents DART, an innovative
automated end-to-end pipeline that revolutionizes object detection workflows
from data collection to model evaluation. It eliminates the need for laborious
human labeling and extensive data collection while achieving outstanding
accuracy across diverse scenarios. DART encompasses four key stages: (1) Data
Diversification using subject-driven image generation (DreamBooth with SDXL),
(2) Annotation via open-vocabulary object detection (Grounding DINO) to
generate bounding box and class labels, (3) Review of generated images and
pseudo-labels by large multimodal models (InternVL-1.5 and GPT-4o) to guarantee
credibility, and (4) Training of real-time object detectors (YOLOv8 and
YOLOv10) using the verified data. We apply DART to a self-collected dataset of
construction machines named Liebherr Product, which contains over 15K
high-quality images across 23 categories. The current instantiation of DART
significantly increases average precision (AP) from 0.064 to 0.832. Its modular
design ensures easy exchangeability and extensibility, allowing for future
algorithm upgrades, seamless integration of new object categories, and
adaptability to customized environments without manual labeling and additional
data collection. The code and dataset are released at
https://github.com/chen-xin-94/DART.
