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Automotive related datasets have previously been used for training autonomousdriving systems or vehicle classification tasks. However, there is a lack ofdatasets in the field of automotive AI for car parts detection, and mostavailable datasets are limited in size and scope, struggling to cover diversescenarios. To address this gap, this paper presents a large-scale andfine-grained automotive dataset consisting of 84,162 images for detecting 12different types of car parts. This dataset was collected from natural camerasand online websites which covers various car brands, scenarios, and shootingangles. To alleviate the burden of manual annotation, we propose a novelsemi-supervised auto-labeling method that leverages state-of-the-artpre-trained detectors. Moreover, we study the limitations of the Grounding DINOapproach for zero-shot labeling. Finally, we evaluate the effectiveness of ourproposed dataset through fine-grained car parts detection by training severallightweight YOLO-series detectors.

A Large-Scale Car Parts (LSCP) Dataset for Lightweight Fine-Grained Detection