Marius Wenning, Anton Backhaus, Tobias Adlon, Peter Burggräf

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.

DOI Link: 
10.21227/gpnv-t367
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[1] Marius Wenning, "Visual Object Detection in Factory Environment", IEEE Dataport, 2022. [Online]. Available: http://dx.doi.org/10.21227/gpnv-t367. Accessed: Sep. 25, 2023.
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title = {Visual Object Detection in Factory Environment},
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Marius Wenning. (2022). Visual Object Detection in Factory Environment. IEEE Dataport. http://dx.doi.org/10.21227/gpnv-t367
Marius Wenning, 2022. Visual Object Detection in Factory Environment. Available at: http://dx.doi.org/10.21227/gpnv-t367.
Marius Wenning. (2022). "Visual Object Detection in Factory Environment." Web.
1. Marius Wenning. Visual Object Detection in Factory Environment [Internet]. IEEE Dataport; 2022. Available from : http://dx.doi.org/10.21227/gpnv-t367
Marius Wenning. "Visual Object Detection in Factory Environment." doi: 10.21227/gpnv-t367