Object Detection for Autonomous Vehicles

References

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  2. Yu, Fisher et al. "BDD 100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning." Cornell University, 8 Apr. 2020, arxiv.org/abs/1805.04687.
  3. "TorchVision Object Detection Finetuning Tutorial." PyTorch, pytorch.org/tutorials/intermediate/torchvision_tutorial.html.
  4. Girshick, Ross et al. "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation." Cornell University, 22 Oct. 2014, arxiv.org/abs/1311.2524.
  5. Girshick, Ross et al. "Fast R-CNN." Cornell University, 27 Sep. 2015, arxiv.org/abs/1504.08083.
  6. Ren, Shaoqing et al. "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks." Cornell University 6 Jan. 2016, arxiv.org/abs/1506.01497.
  7. Redmon, Joseph et al. "You Only Look Once: Unified, Real-Time Object Detection." Cornell University, 9 May 2016, arxiv.org/abs/1506.02640.
  8. Redmon, Joseph and Ali Farhadi. "YOLO9000: Better, Faster, Stronger." Cornell University, 25 Dec. 2016, arxiv.org/abs/1612.08242.
  9. Redmon, Joseph and Ali Farhadi. "YOLOv3: An Incremental Improvement." Cornell University, 8 Apr. 2018, arxiv.org/abs/1804.02767.
  10. Bochkovskiy, Alexey, Wang, Chien-Yao, and Hong-Yuan Mark Liao. "YOLOv4: Optimal Speed and Accuracy of Object Detection." Cornell University, 23 Apr. 2020, arxiv.org/abs/2004.10934.
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  12. Bochkovskiy, Alexey. "Yolo v4, v3 and v2 for Windows and Linux." GitHub, github.com/AlexeyAB/darknet.