Abstract
We propose a fast object detector, based on Convolutional Neural Network (CNN). The object detector, which operates on RGB images, is designed for a mobile robot equipped with a robotic manipulator. The proposed detector is designed to quickly and accurately detect objects which are common in small manufactories and workshops. We propose a fully convolutional architecture of neural network which allows the full GPU implementation. We provide results obtained on our custom dataset based on ImageNet and other common datasets, like COCO or PascalVOC. We also compare the proposed method with other state of the art object detectors.
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Acknowledgments
This work was supported by the NCBR Grant no. LIDER/33/0176/L-8/16/NCBR/2017.
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Piaskowski, K., Belter, D. (2019). Fast Object Detector Based on Convolutional Neural Networks. In: Barneva, R., Brimkov, V., Kulczycki, P., Tavares, J. (eds) Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications. CompIMAGE 2018. Lecture Notes in Computer Science(), vol 10986. Springer, Cham. https://doi.org/10.1007/978-3-030-20805-9_15
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