YOLO

You only look once (YOLO) is a state-of-the-art, real-time object detection system. YOLO: Real-Time Object Detection. Prior detection systems repurpose classifiers or localizers to perform detection. They apply the model to an image at multiple locations and scales. High scoring regions of the image are considered detections. We use a totally different approach. We apply a single neural network to the full image. This network divides the image into regions and predicts bounding boxes and probabilities for each region. These bounding boxes are weighted by the predicted probabilities. Our model has several advantages over classifier-based systems. It looks at the whole image at test time so its predictions are informed by global context in the image. It also makes predictions with a single network evaluation unlike systems like R-CNN which require thousands for a single image. This makes it extremely fast, more than 1000x faster than R-CNN and 100x faster than Fast R-CNN. See our paper for more details on the full system.


References in zbMATH (referenced in 18 articles )

Showing results 1 to 18 of 18.
Sorted by year (citations)

  1. Dognin, Pierre; Melnyk, Igor; Mroueh, Youssef; Padhi, Inkit; Rigotti, Mattia; Ross, Jarret; Schiff, Yair; Young, Richard A.; Belgodere, Brian: Image captioning as an assistive technology: Lessons learned from VizWiz 2020 challenge (2022)
  2. Ostovar, Ahmad; Bensch, Suna; Hellström, Thomas: Natural language guided object retrieval in images (2021)
  3. Philippe Apparicio, David Maignan, Jérémy Gelb: VIFECO: An Open-Source Software for Counting Features on a Video (2021) not zbMATH
  4. Khattar, Sahil; Rama Krishna, C.: Adversarial attack to fool object detector (2020)
  5. Li, Aoxue; Lu, Zhiwu; Guan, Jiechao; Xiang, Tao; Wang, Liwei; Wen, Ji-Rong: Transferrable feature and projection learning with class hierarchy for zero-shot learning (2020)
  6. Liu, Li; Ouyang, Wanli; Wang, Xiaogang; Fieguth, Paul; Chen, Jie; Liu, Xinwang; Pietikäinen, Matti: Deep learning for generic object detection: a survey (2020)
  7. Liu, Zhongchao; Xiao, Dongyue: Recognition method of mature strawberry based on improved SSD deep convolution neural network (2020)
  8. Ma, Liyong; Xie, Wei; Huang, Haibin: Convolutional neural network based obstacle detection for unmanned surface vehicle (2020)
  9. Sharma, Vipul; Mir, Roohie Naaz: A comprehensive and systematic look up into deep learning based object detection techniques: a review (2020)
  10. Teng, Hao; Lu, Huijuan; Ye, Minchao; Yan, Ke; Gao, Zhigang; Jin, Qun: Applying of adaptive threshold non-maximum suppression to pneumonia detection (2020)
  11. Tianming Liu, Haoyu Wang, Li Li, Xiapu Luo, Feng Dong, Yao Guo, Liu Wang, Tegawendé F. Bissyandé, Jacques Klein: MadDroid: Characterising and Detecting Devious Ad Content for Android Apps (2020) arXiv
  12. Chandra, Rohan; Bhattacharya, Uttaran; Roncal, Christian; Bera, Aniket; Manocha, Dinesh: RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputs (2019) arXiv
  13. Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, Juergen Gall: SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences (2019) arXiv
  14. Orest Kupyn, Tetiana Martyniuk, Junru Wu, Zhangyang Wang: DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better (2019) arXiv
  15. Rajan, Purnima; Ma, Yongming; Jedynak, Bruno: Cox processes for counting by detection (2019)
  16. Wang, Sen; Xing, Yuxiang; Zhang, Li; Gao, Hewei; Zhang, Hao: Deep convolutional neural network for ulcer recognition in wireless capsule endoscopy: experimental feasibility and optimization (2019)
  17. Maxime Rousseau; Jean-Marc Retrouvey: pfla: A Python Package for Dental Facial Analysis using Computer Vision and Statistical Shape Analysis (2018) not zbMATH
  18. Zhang, Jianming; Huang, Manting; Jin, Xiaokang; Li, Xudong: A real-time Chinese traffic sign detection algorithm based on modified YOLOv2 (2017)


Further publications can be found at: https://pjreddie.com/publications/