# YOLO for Knowledge Extraction from Vehicle Images: A Baseline Study

arXiv: https://arxiv.org/abs/2507.18966 · HTML: https://mlsanity.com/paper/2507.18966/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 59 references that are in the map.
- First posted on arXiv 25 Jul 2025.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019)
- [YOLO-World: Real-Time Open-Vocabulary Object Detection](https://mlsanity.com/paper/2401.17270/) (2024)
- [TransFG: A Transformer Architecture for Fine-grained Recognition](https://mlsanity.com/paper/2103.07976/) (2021)
- [A Large-Scale Car Dataset for Fine-Grained Categorization and Verification](https://mlsanity.com/paper/1506.08959/) (2015)
- [CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification](https://mlsanity.com/paper/1903.09254/) (2019)
- [Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs](https://mlsanity.com/paper/1909.06161/) (2019)
- [Unsupervised Feature Learning Toward a Real-time Vehicle Make and Model Recognition](https://mlsanity.com/paper/1806.03028/) (2018)

## Abstract (arXiv)

Accurate identification of vehicle attributes such as make, colour, and shape is critical for law enforcement and intelligence applications. This study evaluates the effectiveness of three state-of-the-art deep learning approaches YOLO-v11, YOLO-World, and YOLO-Classification on a real-world vehicle image dataset. This dataset was collected under challenging and unconstrained conditions by NSW Police Highway Patrol Vehicles. A multi-view inference (MVI) approach was deployed to enhance the performance of the models' predictions. To conduct the analyses, datasets with 100,000 plus images were created for each of the three metadata prediction tasks, specifically make, shape and colour. The models were tested on a separate dataset with 29,937 images belonging to 1809 number plates. Different sets of experiments have been investigated by varying the models sizes. A classification accuracy of 93.70%, 82.86%, 85.19%, and 94.86% was achieved with the best performing make, shape, colour, and colour-binary models respectively. It was concluded that there is a need to use MVI to get usable models within such complex real-world datasets. Our findings indicated that the object detection models YOLO-v11 and YOLO-World outperformed classification-only models in make and shape extraction. Moreover, smaller YOLO variants perform comparably to larger counterparts, offering substantial efficiency benefits for real-time predictions. This work provides a robust baseline for extracting vehicle metadata in real-world scenarios. Such models can be used in filtering and sorting user queries, minimising the time required to search large vehicle images datasets.