# Scalable Object Detection in the Car Interior With Vision Foundation Models Source: [Scalable Object Detection in the Car Interior With Vision Foundation Models]() Collection index: [llms.txt]() Data updated: 2026-05-15T05:00:04.680Z Authors: Sebastian Schmidt, B\\'alint M\\'esz\\'aros, Ahmet Firintepe, Stephan G\\"unnemann Published: 2025-08-27T07:58:57.000Z ## Abstract AI tasks in the car interior like identifying and localizing externally introduced objects is crucial for response quality of personal assistants. However, computational resources of on-board systems remain highly constrained, restricting the deployment of such solutions directly within the vehicle. To address this limitation, we propose the novel Object Detection and Localization (ODAL) framework for interior scene understanding. Our approach leverages vision foundation models through a distributed architecture, splitting computational tasks between on-board and cloud. This design overcomes the resource constraints of running foundation models directly in the car. To benchmark model performance, we introduce ODALbench, a new metric for comprehensive assessment of detection and localization.Our analysis demonstrates the framework's potential to establish new standards in this domain. We compare the state-of-the-art GPT-4o vision foundation model with the lightweight LLaVA 1.5 7B model and explore how fine-tuning enhances the lightweight models performance. Remarkably, our fine-tuned ODAL-LLaVA model achieves an ODAL$\_{score}$ of 89%, representing a 71% improvement over its baseline performance and outperforming GPT-4o by nearly 20%. Furthermore, the fine-tuned model maintains high detection accuracy while significantly reducing hallucinations, achieving an ODAL$\_{SNR}$ three times higher than GPT-4o. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()