Soft-NMS — Improving Object Detection with One Line of Code
Navaneeth Bodla,Bharat Singh,Rama Chellappa,Larry S. Davis +3 more
- pp 5562-5570
TLDR
Soft-NMS as mentioned in this paper decays the detection scores of all other objects as a continuous function of their overlap with M. As per the design of the algorithm, if an object lies within the predefined overlap threshold, it leads to a miss.Abstract:
Non-maximum suppression is an integral part of the object detection pipeline. First, it sorts all detection boxes on the basis of their scores. The detection box M with the maximum score is selected and all other detection boxes with a significant overlap (using a pre-defined threshold) with M are suppressed. This process is recursively applied on the remaining boxes. As per the design of the algorithm, if an object lies within the predefined overlap threshold, it leads to a miss. To this end, we propose Soft-NMS, an algorithm which decays the detection scores of all other objects as a continuous function of their overlap with M. Hence, no object is eliminated in this process. Soft-NMS obtains consistent improvements for the coco-style mAP metric on standard datasets like PASCAL VOC2007 (1.7% for both R-FCN and Faster-RCNN) and MS-COCO (1.3% for R-FCN and 1.1% for Faster-RCNN) by just changing the NMS algorithm without any additional hyper-parameters. Using Deformable-RFCN, Soft-NMS improves state-of-the-art in object detection from 39.8% to 40.9% with a single model. Further, the computational complexity of Soft-NMS is the same as traditional NMS and hence it can be efficiently implemented. Since Soft-NMS does not require any extra training and is simple to implement, it can be easily integrated into any object detection pipeline. Code for Soft-NMS is publicly available on GitHub http://bit.ly/2nJLNMu.read more
Citations
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Cusp Pixel Labelling Model for Objects Outline Using R-CNN
TL;DR: A Cusp Pixel Labelled Model with Precise Tuned Outline using Machine Learning (CPLM-PTOML) is proposed in this paper that accurately detects the cusp points of the object in the image by extracting the skeleton of theobject to recognize the exact object resided in theimage.
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Cross-domain Federated Object Detection
TL;DR: A cross-domain federated object detection framework, named FedOD, which first performs the federated training to obtain a public global aggregated model through multi-teacher distillation, and sends the aggregated models back to each client for finetuning its personalized local model.
Book ChapterDOI
Penalty Non-maximum Suppression in Object Detection
Wenqing Zhao,Hai Yan +1 more
TL;DR: Penalty-NMS method is proposed which according to the different overlap to assign penalty coefficient to decay detections scores will obtains significant improvements on standard datasets like PASCAL VOC and MS COCO without any additional computational and parameters.
Journal ArticleDOI
Mds-Net: Multi-Scale Depth Stratification 3d Object Detection from Monocular Images
Proceedings ArticleDOI
Confidence Propagation Cluster: Unleash Full Potential of Object Detectors
TL;DR: Wang et al. as discussed by the authors proposed the Confidence Propagation Cluster (CP-Cluster) to replace NMS-based methods, which is fully parallelizable as well as better in accuracy.
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