# Leveraging Multi-view Data for Improved Detection Performance: An Industrial Use Case

arXiv: https://arxiv.org/abs/2304.08111 · HTML: https://mlsanity.com/paper/2304.08111/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [Eigen-CAM: Class Activation Map using Principal Components](https://mlsanity.com/paper/2008.00299/) (2020)
- [Data-Efficient Graph Embedding Learning for PCB Component Detection](https://mlsanity.com/paper/1811.06994/) (2018)
- [PCB Component Detection using Computer Vision for Hardware Assurance](https://mlsanity.com/paper/2202.08452/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Multi-Camera Vision-Based Approach for Fine-Grained Assembly Quality Control](https://mlsanity.com/paper/2509.23815/) (2025)
- [Object-Centric Data Synthesis for Category-level Object Detection](https://mlsanity.com/paper/2511.23450/) (2025)

## Abstract (arXiv)

Printed circuit boards (PCBs) are essential components of electronic devices, and ensuring their quality is crucial in their production. However, the vast variety of components and PCBs manufactured by different companies makes it challenging to adapt to production lines with speed demands. To address this challenge, we present a multi-view object detection framework that offers a fast and precise solution. We introduce a novel multi-view dataset with semi-automatic ground-truth data, which results in significant labeling resource savings. Labeling PCB boards for object detection is a challenging task due to the high density of components and the small size of the objects, which makes it difficult to identify and label them accurately. By training an object detector model with multi-view data, we achieve improved performance over single-view images. To further enhance the accuracy, we develop a multi-view inference method that aggregates results from different viewpoints. Our experiments demonstrate a 15% improvement in mAP for detecting components that range in size from 0.5 to 27.0 mm.