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Object detection

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Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes

Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.[1] Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.

Uses

Detection of objects on a road

It is widely used in computer vision tasks such as image annotation,[2] vehicle counting,[3] A simple and straightforward solution of reducing the domain gap is to apply an image-to-image translation approach, such as cycle-GAN.[4] Among other uses, cross-domain object detection is applied in autonomous driving, where models can be trained on a vast amount of video game scenes, since the labels can be generated without manual labor.

Concept

Evffttyftyfyttytuguyut e equal side lengths are needed. A similar approach is used for face identification where eyes, nose, and lips can be found and features like skin color and distance between eyes can be found.

Methods

Methods for object detection generally fall into either neural network-based or non-neural approaches. For non-neural approaches, it becomes necessary to first define features using one of the methods below, then using a technique such as support vector machine (SVM) to do the classification. On the other hand, neural techniques are able to do end-to-end object detection without specifically defining features, and are typically based on convolutional neural networks (CNN).

See also

References

  1. ^ Dasiopoulou, Stamatia, et al. "Knowledge-assisted semantic video object detection." IEEE Transactions on Circuits and Systems for Video Technology 15.10 (2005): 1210–1224.
  2. ^ Ling Guan; Yifeng He; Sun-Yuan Kung (1 March 2012). Multimedia Image and Video Processing. CRC Press. pp. 331–. ISBN 978-1-4398-3087-1.
  3. ^ Alsanabani, Ala; Ahmed, Mohammed; AL Smadi, Ahmad. AWADA: Attention-Weighted Adversarial Domain Adaptation for Object Detection. arXiv:2208.14662 [cs.CV]. {{cite book}}: Unknown parameter |class= ignored (help)
  4. ^ Zhu, Jun-Yan; Park, Taesung; Isola, Phillip; Efros, Alexei A. (2020-08-24). "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks". arXiv:1703.10593 [cs.CV].
  5. ^ Dalal, Navneet (2005). "Histograms of oriented gradients for human detection" (PDF). Computer Vision and Pattern Recognition. 1.
  6. ^ Ross, Girshick (2014). "Rich feature hierarchies for accurate object detection and semantic segmentation" (PDF). Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE. pp. 580–587. arXiv:1311.2524. doi:10.1109/CVPR.2014.81. ISBN 978-1-4799-5118-5. S2CID 215827080.
  7. ^ Girschick, Ross (2015). "Fast R-CNN" (PDF). Proceedings of the IEEE International Conference on Computer Vision. pp. 1440–1448. arXiv:1504.08083. Bibcode:2015arXiv150408083G.
  8. ^ Shaoqing, Ren (2015). "Faster R-CNN". Advances in Neural Information Processing Systems. arXiv:1506.01497.
  9. ^ a b Pang, Jiangmiao; Chen, Kai; Shi, Jianping; Feng, Huajun; Ouyang, Wanli; Lin, Dahua (2019-04-04). "Libra R-CNN: Towards Balanced Learning for Object Detection". arXiv:1904.02701v1 [cs.CV].
  10. ^ Liu, Wei (October 2016). "SSD: Single shot multibox detector". Computer Vision – ECCV 2016. Lecture Notes in Computer Science. Vol. 9905. pp. 21–37. arXiv:1512.02325. doi:10.1007/978-3-319-46448-0_2. ISBN 978-3-319-46447-3. S2CID 2141740. {{cite book}}: |journal= ignored (help)
  11. ^ Zhang, Shifeng (2018). "Single-Shot Refinement Neural Network for Object Detection". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4203–4212. arXiv:1711.06897. Bibcode:2017arXiv171106897Z.
  12. ^ Lin, Tsung-Yi (2020). "Focal Loss for Dense Object Detection". IEEE Transactions on Pattern Analysis and Machine Intelligence. 42 (2): 318–327. arXiv:1708.02002. Bibcode:2017arXiv170802002L. doi:10.1109/TPAMI.2018.2858826. PMID 30040631. S2CID 47252984.
  13. ^ Zhu, Xizhou (2018). "Deformable ConvNets v2: More Deformable, Better Results". arXiv:1811.11168 [cs.CV].
  14. ^ Dai, Jifeng (2017). "Deformable Convolutional Networks". arXiv:1703.06211 [cs.CV].
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