DART: An Automated End-to-End Object Detection Pipeline with Data Diversification, Open-Vocabulary Bounding Box Annotation, Pseudo-Label Review, and Model Training

• URL: http://arxiv.org/abs/2407.09174v1
• Date: Fri, 12 Jul 2024 11:16:44 GMT
• Title: DART: An Automated End-to-End Object Detection Pipeline with Data Diversification, Open-Vocabulary Bounding Box Annotation, Pseudo-Label Review, and Model Training
• Authors: Chen Xin, Andreas Hartel, Enkelejda Kasneci,
• Abstract summary: This paper presents DART, an automated end-to-end pipeline for object detection. DART eliminates the need for human labeling and extensive data collection while excelling in diverse scenarios. The current implementation of DART significantly increases average precision (AP) from 0.064 to 0.832.
• Score: 8.705939889424558
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Swift and accurate detection of specified objects is crucial for many industrial applications, such as safety monitoring on construction sites. However, traditional approaches rely heavily on arduous manual annotation and data collection, which struggle to adapt to ever-changing environments and novel target objects. To address these limitations, this paper presents DART, an automated end-to-end pipeline designed to streamline the entire workflow of an object detection application from data collection to model deployment. DART eliminates the need for human labeling and extensive data collection while excelling in diverse scenarios. It employs a subject-driven image generation module (DreamBooth with SDXL) for data diversification, followed by an annotation stage where open-vocabulary object detection (Grounding DINO) generates bounding box annotations for both generated and original images. These pseudo-labels are then reviewed by a large multimodal model (GPT-4o) to guarantee credibility before serving as ground truth to train real-time object detectors (YOLO). 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 implementation of DART significantly increases average precision (AP) from 0.064 to 0.832. Furthermore, we adopt a modular design for DART to ensure easy exchangeability and extensibility. This allows for a smooth transition to more advanced algorithms in the future, seamless integration of new object categories without manual labeling, and adaptability to customized environments without extra data collection. The code and dataset are released at https://github.com/chen-xin-94/DART.

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