Review of Typical Vehicle Detection Algorithms Based on Deep Learning

References (47)

1. 1 ImageNet classification with deep convolutional neural networks [DOI]
2. 2 Exploiting Generative AI to Scale up Intelligent Tutoring Systems [DOI]
3. 3 You Only Look Once: Unified, Real-Time Object Detection [DOI]
4. 4 Swin Transformer: Hierarchical Vision Transformer using Shifted Windows [DOI]
5. 5 Histograms of Oriented Gradients for Human Detection [DOI]
6. 6 Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation [DOI]
7. 7 SSD: Single Shot MultiBox Detector
8. 8 Feature Pyramid Networks for Object Detection [DOI]
9. 9 YOLO9000: Better, Faster, Stronger [DOI]
10. 10 Rapid object detection using a boosted cascade of simple features [DOI]
11. 11 End-to-End Object Detection with Transformers [DOI]
12. 12 A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper) [DOI]
13. 13 Backpropagation Applied to Handwritten Zip Code Recognition [DOI]
14. 14 YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors [DOI]
15. 15 Non-local Neural Networks [DOI]
16. 16 Effective Approaches to Attention-based Neural Machine Translation [DOI]
17. 17 A Non-Local Algorithm for Image Denoising [DOI]
18. 18 Deformable Convolutional Networks [DOI]
19. 19 Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions [DOI]
20. 20 A Survey on Vision Transformer [DOI]
21. 21 Transformers in Vision: A Survey [DOI]
22. 22 Masked-attention Mask Transformer for Universal Image Segmentation [DOI]
23. 23 Locality-sensitive hashing scheme based on p-stable distributions [DOI]
24. 24 A discriminatively trained, multiscale, deformable part model [DOI]
25. 25 Efficient Non-Maximum Suppression [DOI]
26. 26 Transformers in medical imaging: A survey [DOI]
27. 27 End-to-End Dense Video Captioning with Masked Transformer [DOI]
28. 28 UP-DETR: Unsupervised Pre-training for Object Detection with Transformers [DOI]
29. 29 A Survey of Visual Transformers [DOI]
30. 30 Enhancement of SSD by concatenating feature maps for object detection [DOI]
31. 31 Learning Joint Spatial-Temporal Transformations for Video Inpainting [DOI]
32. 32 Video Transformers: A Survey [DOI]
33. 33 Very Deep Convolutional Networks for Large-Scale Image Recognition [DOI]
34. 34 An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale [DOI]
35. 35 YOLOv4: Optimal Speed and Accuracy of Object Detection [DOI]
36. 36 YOLOX: Exceeding YOLO Series in 2021 [DOI]
37. 37 Convolutional Sequence to Sequence Learning [DOI]
38. 38 Proceedings of IEEE International Conference on Computer Vision [DOI]
39. 39 Deformable DETR: Deformable Transformers for End-to-End Object Detection [DOI]
40. 40 YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications [DOI]
41. 41 Exploring the Limits of Language Modeling [DOI]
42. 42 FSSD: Feature Fusion Single Shot Multibox Detector [DOI]
43. 43 Efficient DETR: Improving End-to-End Object Detector with Dense Prior [DOI]
44. 44 TransGAN: Two Transformers Can Make One Strong GAN
45. 45 Toward Transformer-Based Object Detection [DOI]
46. 46 Demystifying Local Vision Transformer: Sparse Connectivity, Weight Sharing, and Dynamic Weight
47. 47 Learning Joint Spatial-Temporal Transformations for Video Inpainting [DOI]

Cited by 2

Related research

• Isolation and Identification of Bacteria from Transformer Oil Contaminated Soil — shares topic coverage
• Construction of a Simple Transformer to Illustrate Faraday’s Law of Electromagnetic Induction along Side Mutual Inductance — shares topic coverage
• Advanced Sequential Learning Models for IoT Intrusion Detection: A Comparative Analysis of Transformer, GRU and LSTM Architectures — shares topic coverage
• Application of LSTM and Transformer Hybrid Model for Electricity Consumption Forecasting — shares topic coverage
• Multimodal Emotion Recognition Using Explainable Hybrid CNN-Transformer Networks — shares topic coverage