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[1910.10949] ROBO: Robust, Fully Neural Object Detection for Robot Soccer
For instance, the network could be trained to detect the edges of the soccer field and ignore objects well outside., This way we could help the network learn by reducing the interference of complex backgrounds outside the field
Abstract: Deep Learning has become exceptionally popular in the last few years due to
its success in computer vision and other fields of AI. However, deep neural
networks are computationally expensive, which limits their application in low
power embedded systems, such as mobile robots. In this paper, an efficient
neural network architecture is proposed for the problem of detecting relevant
objects in robot soccer environments. The ROBO model's increase in efficiency
is achieved by exploiting the peculiarities of the environment. Compared to the
state-of-the-art Tiny YOLO model, the proposed network provides approximately
35 times decrease in run time, while achieving superior average precision,
although at the cost of slightly worse localization accuracy.
‹ (Exploiting the environment)›
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Related: TFIDF
[1611.05664] Learning to detect and localize many objects from few examples[1803.10862] A Survey on Deep Learning Methods for Robot Vision[1312.6229] OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks[1808.02518] Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning[1506.02640] You Only Look Once: Unified, Real-Time Object Detection[1701.04693] Incremental Learning for Robot Perception through HRI[1704.06857] A Review on Deep Learning Techniques Applied to Semantic Segmentation[1905.05241] Deep Neural Networks for Marine Debris Detection in Sonar Images
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Related: TFIDF
[1611.05664] Learning to detect and localize many objects from few examples[1803.10862] A Survey on Deep Learning Methods for Robot Vision[1312.6229] OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks[1808.02518] Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning[1506.02640] You Only Look Once: Unified, Real-Time Object Detection[1701.04693] Incremental Learning for Robot Perception through HRI[1704.06857] A Review on Deep Learning Techniques Applied to Semantic Segmentation[1905.05241] Deep Neural Networks for Marine Debris Detection in Sonar Images