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An Analysis of the Performance of Lightweight CNNs in the Context of Object Detection on Mobile Phones
Abstract
Convolutional Neural Networks (CNNs) are widely used in computer vision, which is now increasingly used in mobile phones. The problem is that smartphones do not have much processing power. Initially, CNNs focused solely on increasing accuracy. High-end computing devices are most often used in this type of research. The most popular application of lightweight CNN object detection is real-time image processing, which can be found in devices such as cameras and autonomous vehicles. Therefore, there is a need to optimize CNNs for use on mobile devices. This paper presents the comparision of latency and mAP of 22 lightweight CNN models from the MobileNet and EfficientDet families measured on 7 mobile phones.
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Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Łęcki J., Hering M., Jabłoński M., Karpus A.: An Analysis of the Performance of Lightweight CNNs in the Context of Object Detection on Mobile Phones// / : , 2024,
- DOI:
- Digital Object Identifier (open in new tab) 10.62036/isd.2024.12
- Sources of funding:
-
- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
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