Visual Object Detection in Factory Environment

Automated driving in public traffic still faces many technical and legal challenges. However, automating vehicles at low speeds in controlled industrial environments is already achievable today. A reliable obstacle detection is mandatory to prevent accidents. Recent advances in convolutional neural network-based algorithms have made it conceivable to replace distance measuring laser scanners with common monocameras. In this paper, we present a photorealistic 3D simulated factory environment for testing vision-based obstacle detecting algorithms preceding field tests on the safety-critical system. We further test two obstacle detection methods employing state-of-the-art semantic segmentation and depth estimation in a range of challenging test scenarios. Both models performed well under normal factory settings. Some edge cases, however, lead to vehicle crashes.