A Lightweight Region Proposal Network for Task Specific Applications
| Autor: | Rudi Villing, Simon O'Keeffe |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science Image processing 02 engineering and technology 010501 environmental sciences Object (computer science) Autonomous robot 01 natural sciences Convolutional neural network Pipeline (software) Task (project management) Image (mathematics) Bounding overwatch 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | 2019 30th Irish Signals and Systems Conference (ISSC). |
| DOI: | 10.1109/issc.2019.8904949 |
| Popis: | Quickly and cheaply finding areas of interest within an image can save computationally intensive image processing in a vision pipeline. Existing region proposal networks are either too general (finding all objects in an image) or too complex (providing fine-tuned bounding boxes for classification). We propose a straightforward region proposal network that simply scores parts of the image based on whether or not they contain an object of interest. This calculation can be carried out quickly and for many applications including autonomous driving only a small fraction of of the image area may contain objects of interest. We trained our network on an autonomous robot soccer dataset with similar characteristics to the popular KITTI autonomous driving dataset and achieved a recall greater than 95% while eliminating on average over 80% of the image area from further processing. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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