Vehicle Head and Tail Recognition Algorithm for Lightweight DCDSNet
Source abstract
Abstract
In the model of the vehicle recognition algorithm implemented by the convolutional neural network, the model needs to compute and store a lot of parameters. Too many parameters occupy a lot of computational resources making i... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Vehicle License Plate Recognition · Advanced Algorithms and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (33)
Show source-provided references
1. Intelligent transportation and control systems using data mining and machine learning techniques: A comprehensive study10.1109/ACCESS.2019.2909114 ↗
2. Data-driven intelligent transportation systems: A survey10.1109/TITS.2011.2158001 ↗
3. Intelligent transportation system(ITS): Concept, challenge and opportunity
4. Research on intelligent transportation system technologies and applications
5. Technological developments in the intelligent transportation system (ITS)
6. Intelligent bus monitoring and management system
7. Research on mine vehicle tracking and detection technology based on YOLOv510.1080/21642583.2022.2057370 ↗
8. ImageNet classification with deep convolutional neural networks10.1145/3065386 ↗
9. Visualizing and understanding convolutional networks
10. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” Sep. 2014, arxiv:1409.1556.
11. Deep residual learning for image recognition
12. Densely connected convolutional networks
13. A. G. Howard et al., “MobileNets: Efficient convolutional neural networks for mobile vision applications,” Apr. 2017, arxiv:1704.04861.
14. Deep convolution neural network based research on recognition of mine vehicle head and tail
15. Going deeper with convolutions
16. Rethinking the inception architecture for computer vision
17. Rich feature hierarchies for accurate object detection and semantic segmentation
18. Fast R-CNN
19. You only look once: Unified, real-time object detection
20. CSPNet: A new backbone that can enhance learning capability of CNN
21. MobileNetV2: Inverted residuals and linear bottlenecks
22. ShuffleNet v2: Practical guidelines for efficient cnn architecture design
23. CenterMask: Real-time anchor-free instance segmentation
24. Faster R-CNN: Towards real-time object detection with region proposal networks10.1109/TPAMI.2016.2577031 ↗
25. ShuffleNet: An extremely efficient convolutional neural network for mobile devices
26. F. N. Iandola, S. Han, MW. Moskewicz, K. Ashraf, WJ. Dally and K. Keutzer, “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size,” Feb. 2016, arxiv:1602.07360.
27. Densely connected graph convolutional networks for graph-to-sequence learning10.1162/tacl_a_00269 ↗
28. Understanding of a convolutional neural network
29. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs10.1109/TPAMI.2017.2699184 ↗
30. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus and Y. LeCun, “Integrated recognition, localization and detection using convolutional networks,” Dec. 2013, arxiv:1312.6229.
31. CNN with depthwise separable convolutions and combined kernels for rating prediction10.1016/j.eswa.2020.114528 ↗
32. Semantic segmentation of remote sensing images using transfer learning and deep convolutional neural network with dense connection10.1109/ACCESS.2020.3003914 ↗
33. Lightweight and scale adaptive efficient backbone network for recognition