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Accurate and Efficient LIF-Nets for 3D Detection and Recognition
- Source :
- IEEE Access, Vol 8, Pp 98562-98571 (2020)
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- 3D object detection and recognition are crucial tasks for many spatiotemporal processing applications, such as computer-aided diagnosis and autonomous driving. Although prevalent 3D Convolution Nets (ConvNets) have continued to improve the accuracy and sensitivity, excessive computing resources are required. In this paper, we propose Leaky Integrate and Fire Networks (LIF-Nets) for 3D detection and recognition tasks. LIF-Nets have rich inter-frame sensing capability brought from membrane potentials, and low power event-driven mechanism, which make them excel in 3D processing and save computational cost at the same time. We also develop ResLIF Blocks to solve the degradation problem of deep LIF-Nets, and employ U-LIF structure to improve the feature representation capability. As a result, we carry out experiments on the LUng Nodule Analysis 2016 (LUNA16) public dataset for chest CT automated analysis and conclude that the LIF-Nets achieve 94.6% detection sensitivity at 8 False Positives per scan and 94.14% classification accuracy while the LIF-detection net reduces 65.45% multiplication operations, 65.12% addition operations, and 65.32% network parameters. The results show that LIF-Nets have extraordinary time-efficient and energy-saving performance while achieving comparable accuracy.
- Subjects :
- General Computer Science
Computer science
Feature extraction
02 engineering and technology
030218 nuclear medicine & medical imaging
03 medical and health sciences
3D recognition
0302 clinical medicine
3D detection
Spiking neural network
0202 electrical engineering, electronic engineering, information engineering
False positive paradox
General Materials Science
Sensitivity (control systems)
Representation (mathematics)
business.industry
General Engineering
leaky integrate and fire model
Pattern recognition
pulmonary nodule screening
Object detection
Feature (computer vision)
020201 artificial intelligence & image processing
lcsh:Electrical engineering. Electronics. Nuclear engineering
Artificial intelligence
business
lcsh:TK1-9971
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....41d1e883988cb00693c011de76c64dd9