Detection of Package Edges in Distance Maps. Vasileva, E., Avramovski, N., & Ivanovski, Z. In 2020 28th European Signal Processing Conference (EUSIPCO), pages 600-604, Aug, 2020. ![Detection of Package Edges in Distance Maps [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper doi abstract bibtex This paper presents a CNN-based algorithm for detecting package edges in a scene represented with a distance map (range image), trained on a custom dataset of packaging scenarios. The proposed algorithm represents the basis for package recognition for automatic trailer loading/unloading. The main focus of this paper is designing a semantic segmentation CNN model capable of detecting different types of package edges in a distance map containing distance errors characteristic of Time-of-Flight (ToF) scanning, and differentiating box edges from edges belonging to other types of packaging objects (bags, irregular objects, etc.). The proposed CNN is optimized for training with a limited number of samples containing heavily imbalanced classes. Generating a binary mask of edges with 1-pixel thickness from the probability maps outputted from the CNN is achieved through a custom non-maximum suppression-based edge thinning algorithm. The proposed algorithm shows promising results in detecting box edges.
@InProceedings{9287558,
author = {E. Vasileva and N. Avramovski and Z. Ivanovski},
booktitle = {2020 28th European Signal Processing Conference (EUSIPCO)},
title = {Detection of Package Edges in Distance Maps},
year = {2020},
pages = {600-604},
abstract = {This paper presents a CNN-based algorithm for detecting package edges in a scene represented with a distance map (range image), trained on a custom dataset of packaging scenarios. The proposed algorithm represents the basis for package recognition for automatic trailer loading/unloading. The main focus of this paper is designing a semantic segmentation CNN model capable of detecting different types of package edges in a distance map containing distance errors characteristic of Time-of-Flight (ToF) scanning, and differentiating box edges from edges belonging to other types of packaging objects (bags, irregular objects, etc.). The proposed CNN is optimized for training with a limited number of samples containing heavily imbalanced classes. Generating a binary mask of edges with 1-pixel thickness from the probability maps outputted from the CNN is achieved through a custom non-maximum suppression-based edge thinning algorithm. The proposed algorithm shows promising results in detecting box edges.},
keywords = {Training;Visualization;Image edge detection;Semantics;Signal processing algorithms;Packaging;Signal processing;edge detection;semantic segmentation;depth maps;CNN;package recognition;automatic unloading},
doi = {10.23919/Eusipco47968.2020.9287558},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2020/pdfs/0000600.pdf},
}
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