An Efficient Object Detection Algorithm Based on Compressed Networks
Open accessAbstract
For a long time, object detection has been a popular but difficult research problem in the field of pattern recognition. In recent years, object detection algorithms based on convolutional neural networks have achieved excellent results. However, neural networks are computationally intensive and parameter redundant, so they are difficult to deploy on resource-limited embedded devices. Especially for two-stage detectors, operations and parameters are mainly clustered on feature fusion of proposals after the region of interest (ROI) pooling layer, and they are enormous. In order to deal with these problems, we propose a subnetwork—efficient feature fusion module (EFFM) to reduce the number of operations and parameters for a two-stage detector. In addition, we propose a multi-scale dilation region proposal network (RPN) to further improve detection accuracy. Finally, our accuracy is higher than Faster RCNN based on VGG16, the number of operations is only half of the latter, and the number of parameters is only one third.
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Detectors such as SqueezeNet or [55] are optimized to run on embedded hardware, have a smaller neural network architecture, making it possible to detect objects using a reduced number of operations, at the cost of detection accuracy.
“If in-vehicle computation resources are scarce, one can use detectors such as SqueezeNet [40] or [55], which are optimized to run on embedded hardware. These detectors usually have a smaller neural network architecture, making it possible to detect objects using a reduced number of operations, at the cost of detection accuracy.”
From A survey of deep learning techniques for autonomous driving (2020) · cites this paper as [55]
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- Authors
- Li, J., Peng, K., & Chang, C.-C.
- Affiliations
- Hangzhou Dianzi University (CN)
- Feng Chia University (TW)
- Corresponding author
- Li, Jianjun
- Chang, Chin-Chen
- Venue
- Symmetry
- Publisher
- MDPI AG
- Published
- 2018-06-22
- Volume
- 10
- Issue
- 7
- Pages
- 235
- ISSN
- 2073-8994 (ISSN-L)
- OpenAlex
- W2809532002
- Cited by
- 6
- References
- 29
- Citations by year
- 2026: 1 · 2023: 1 · 2022: 1 · 2020: 1 · 2019: 1 · 2018: 1
- FWCI
- 0.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Symmetry (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- object detection, two-stage detection, region proposal network, ROI pooling, computational efficiency, parameter reduction, embedded deployment, Faster R-CNN
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