Testing the reliability of monocular obstacle detection methods in a simulated 3D 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...
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| Veröffentlicht in: | Journal of intelligent manufacturing 2022-10, Vol.33 (7), p.2157-2165 |
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| container_title | Journal of intelligent manufacturing |
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| creator | Wenning, Marius Backhaus, Anton Akira Adlon, Tobias Burggräf, Peter |
| description | 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 hypothetically allow the replacement of 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 common factory settings. Some edge cases, however, lead to vehicle crashes. |
| doi_str_mv | 10.1007/s10845-022-01983-4 |
| format | Article |
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| subjects | Accident prevention Algorithms Artificial neural networks Automation Business and Management Computer simulation Control Crashes Datasets Distance measurement Driving ability Field tests Machines Manufacturing Mechatronics Neural networks Obstacle avoidance Plant reliability Processes Production Robotics Semantic segmentation |
| title | Testing the reliability of monocular obstacle detection methods in a simulated 3D factory environment |
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