Name
Title | ||
|---|---|---|
Real-Time Object Detection Via Pruning And A Concatenated Multi-Feature Assisted Region Proposal Network |
Abstract | ||
|---|---|---|
Object detection is an important research area in the field of computer vision. Its purpose is to find all objects in an image and recognize the class of each object. Since the development of deep learning, an increasing number of studies have applied deep learning in object detection and have achieved successful results. For object detection, there are two types of network architectures: one-stage and two-stage. This study is based on the widely-used two-stage architecture, called Faster R-CNN, and our goal is to improve the inference time to achieve real-time speed without losing accuracy.First, we use pruning to reduce the number of parameters and the amount of computation, which is expected to reduce accuracy as a result. Therefore, we propose a multi-feature assisted region proposal network composed of assisted multi-feature concatenation and a reduced region proposal network to improve accuracy. Assisted multi-feature concatenation combines feature maps from different convolutional layers as inputs for a reduced region proposal network. With our proposed method, the network can find regions of interest ( ROIs) more accurately. Thus, it compensates for loss of accuracy due to pruning. Finally, we use ZF-Net and VGG16 as backbones, and test the network on the PASCAL VOC 2007 dataset. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8683842 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Object Detection, Convolutional Neuron Network Compression, Pruning, Region Proposal Network (RPN), Feature Maps Concatenation | Object detection,Architecture,Pattern recognition,Computer science,Inference,Network architecture,Concatenation,Artificial intelligence,Deep learning,Computation,Pruning | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kuan-Hung Shih | 1 | 4 | 0.81 |
| Ching-Te Chiu | 2 | 304 | 38.60 |
| Yen-Yu Pu | 3 | 0 | 0.34 |