Detection of Package Edges in Distance Maps
| Autor: | Elena Vasileva, Nenad Avramovski, Zoran Ivanovski |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Basis (linear algebra)
Computer science business.industry 020206 networking & telecommunications Pattern recognition 02 engineering and technology Visualization Image (mathematics) Computer Science::Computer Vision and Pattern Recognition 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Artificial intelligence Focus (optics) business Distance transform |
| Zdroj: | EUSIPCO |
| DOI: | 10.23919/eusipco47968.2020.9287558 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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