Investigating the Potential of Network Optimization for a Constrained Object Detection Problem
Object detection models are usually trained and evaluated on highly complicated, challenging academic datasets, which results in deep networks requiring lots of computations. However, a lot of operational use-cases consist of more constrained situations: they have a limited number of classes to be d...
Main Authors: | Tanguy Ophoff, Cédric Gullentops, Kristof Van Beeck, Toon Goedemé |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-04-01
|
Series: | Journal of Imaging |
Subjects: | |
Online Access: | https://www.mdpi.com/2313-433X/7/4/64 |
Similar Items
-
Exploring RGB+Depth Fusion for Real-Time Object Detection
by: Tanguy Ophoff, et al.
Published: (2019-02-01) -
Weight pruning-UNet: Weight pruning UNet with depth-wise separable convolutions for semantic segmentation of kidney tumors
by: Patike Kiran Rao, et al.
Published: (2022-01-01) -
A Channel Pruning Algorithm Based on Depth-Wise Separable Convolution Unit
by: Ke Zhang, et al.
Published: (2019-01-01) -
Vehicle and Vessel Detection on Satellite Imagery: A Comparative Study on Single-Shot Detectors
by: Tanguy Ophoff, et al.
Published: (2020-04-01) -
A Lightweight and Accurate UAV Detection Method Based on YOLOv4
by: Hao Cai, et al.
Published: (2022-09-01)