SSD

SSD: Single Shot MultiBox Detector. We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. Our SSD model is simple relative to methods that require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stage and encapsulates all computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on the PASCAL VOC, MS COCO, and ILSVRC datasets confirm that SSD has comparable accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. Compared to other single stage methods, SSD has much better accuracy, even with a smaller input image size. For 300×300 input, SSD achieves 72.1% mAP on VOC2007 test at 58 FPS on a Nvidia Titan X and for 500×500 input, SSD achieves 75.1% mAP, outperforming a comparable state of the art Faster R-CNN model.


References in zbMATH (referenced in 24 articles )

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  1. Goncharenko, V. I.; Zheltov, S. Yu.; Knyaz, V. A.; Lebedev, G. N.; Mikhaylin, D. A.; Tsareva, O. Yu.: Intelligent system for planning group actions of unmanned aircraft in observing mobile objects on the ground in the specified area (2021)
  2. Jia, Fan; Liu, Jun; Tai, Xue-Cheng: A regularized convolutional neural network for semantic image segmentation (2021)
  3. Koo, Bongyeong; Choi, Han-Soo; Kang, Myungjoo: Simple feature pyramid network for weakly supervised object localization using multi-scale information (2021)
  4. Luo, Wenhan; Xing, Junliang; Milan, Anton; Zhang, Xiaoqin; Liu, Wei; Kim, Tae-Kyun: Multiple object tracking: a literature review (2021)
  5. Peng, Jianzhong; Zhu, Wei; Liang, Qiaokang; Li, Zhengwei; Lu, Maoying; Sun, Wei; Wang, Yaonan: Defect detection in code characters with complex backgrounds based on BBE (2021)
  6. Suchan, Jakob; Bhatt, Mehul; Varadarajan, Srikrishna: Commonsense visual sensemaking for autonomous driving -- on generalised neurosymbolic online abduction integrating vision and semantics (2021)
  7. Wu, Zhenni; Chen, Hengxin; Fang, Bin; Li, Zihao; Chen, Xinrun: Building pose estimation from the perspective of UAVs based on CNNs (2021)
  8. Xin Chen, Anqi Pang, Wei Yang, Yuexin Ma, Lan Xu, Jingyi Yu: SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos (2021) arXiv
  9. Amosov, O. S.; Amosova, S. G.; Zhiganov, S. V.; Ivanov, Yu. S.; Pashchenko, F. F.: Computational method for recognizing situations and objects in the frames of a continuous video stream using deep neural networks for access control systems (2020)
  10. Chen, Liqiong; Zou, Lian; Fan, Cien; Liu, Yifeng: Feature weighting network for aircraft engine defect detection (2020)
  11. Chen, Ruidian; He, Jingsong: Two-stage training method of retinanet for bird’s nest detection (2020)
  12. Chigrinskii, V. V.; Matveev, I. A.: Optimization of a tracking system based on a network of cameras (2020)
  13. Daniel Bolya, Sean Foley, James Hays, Judy Hoffman: TIDE: A General Toolbox for Identifying Object Detection Errors (2020) arXiv
  14. Liu, Zichuan; Lin, Guosheng; Goh, Wang Ling: Bottom-up scene text detection with Markov clustering networks (2020)
  15. Ma, Liyong; Xie, Wei; Huang, Haibin: Convolutional neural network based obstacle detection for unmanned surface vehicle (2020)
  16. Teng, Hao; Lu, Huijuan; Ye, Minchao; Yan, Ke; Gao, Zhigang; Jin, Qun: Applying of adaptive threshold non-maximum suppression to pneumonia detection (2020)
  17. Grant, W. Shane; Itti, Laurent: Learning invariant features in modulatory networks through conflict and ambiguity (2019)
  18. 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)
  19. Haifeng Jin, Qingquan Song, Xia Hu: Auto-Keras: An Efficient Neural Architecture Search System (2018) arXiv
  20. Liao, Minghui; Shi, Baoguang; Bai, Xiang: TextBoxes++: a single-shot oriented scene text detector (2018)

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