A novel method for object detection using deep learning and CAD models

TL;DR

This paper presents an automated deep learning-based object detection method that leverages CAD models and synthetic data generation to reduce costs and improve industrial quality control processes.

Contribution

It introduces a fully automated pipeline that creates training datasets from CAD models using Blender, enabling effective object detection without real-world data collection.

Findings

The method achieves high accuracy on real images using only synthetic training data.

It simplifies the adaptation process for new objects in industrial settings.

Experimental validation on practical examples demonstrates its effectiveness.

Abstract

Object Detection (OD) is an important computer vision problem for industry, which can be used for quality control in the production lines, among other applications. Recently, Deep Learning (DL) methods have enabled practitioners to train OD models performing well on complex real world images. However, the adoption of these models in industry is still limited by the difficulty and the significant cost of collecting high quality training datasets. On the other hand, when applying OD to the context of production lines, CAD models of the objects to be detected are often available. In this paper, we introduce a fully automated method that uses a CAD model of an object and returns a fully trained OD model for detecting this object. To do this, we created a Blender script that generates realistic labeled datasets of images containing the object, which are then used for training the OD model. The method is validated experimentally on two practical examples, showing that this approach can generate OD models performing well on real images, while being trained only on synthetic images. The proposed method has potential to facilitate the adoption of object detection models in industry as it is easy to adapt for new objects and highly flexible. Hence, it can result in significant costs reduction, gains in productivity and improved products quality.