CRAFT Objects from Images

Yang, Bin; Yan, Junjie; Lei, Zhen; Li, Stan Z.

doi:10.1109/cvpr.2016.650

Cited by 112 publications

(65 citation statements)

References 40 publications

(72 reference statements)

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“…The multi-region detector [9] introduced iterative bounding box regression, where a R-CNN is applied several times, to produce better bounding boxes. CRAFT [33] and AttractioNet [10] used a multi-stage procedure to generate accurate proposals, and forwarded them to a Fast-RCNN. [19,25] embedded the classic cascade architecture of [31] in object detection networks.…”

Section: Related Workmentioning

confidence: 99%

Cascade R-CNN: Delving Into High Quality Object Detection

Cai

Vasconcelos

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

4,747

2,558

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In object detection, an intersection over union (IoU) threshold is required to define positives and negatives. An object detector, trained with low IoU threshold, e.g. 0.5, usually produces noisy detections. However, detection performance tends to degrade with increasing the IoU thresholds. Two main factors are responsible for this: 1) overfitting during training, due to exponentially vanishing positive samples, and 2) inference-time mismatch between the IoUs for which the detector is optimal and those of the input hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, is proposed to address these problems. It consists of a sequence of detectors trained with increasing IoU thresholds, to be sequentially more selective against close false positives. The detectors are trained stage by stage, leveraging the observation that the output of a detector is a good distribution for training the next higher quality detector. The resampling of progressively improved hypotheses guarantees that all detectors have a positive set of examples of equivalent size, reducing the overfitting problem. The same cascade procedure is applied at inference, enabling a closer match between the hypotheses and the detector quality of each stage. A simple implementation of the Cascade R-CNN is shown to surpass all single-model object detectors on the challenging COCO dataset. Experiments also show that the Cascade R-CNN is widely applicable across detector architectures, achieving consistent gains independently of the baseline detector strength. The code will be made available at https://github.com/zhaoweicai/cascade-rcnn.

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Section: Related Workmentioning

confidence: 99%

Cascade R-CNN: Delving Into High Quality Object Detection

Cai

Vasconcelos

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

4,747

2,558

View full text Add to dashboard Cite

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“…The multi-region detector of [17] introduced iterative bounding box regression, where a R-CNN is applied several times to produce successively more accurate bounding boxes. [18], [19], [60] used a multi-stage procedure to generate accurate proposals, which are forwarded to an accurate model (e.g. Fast R-CNN).…”

Section: Related Workmentioning

confidence: 99%

Cascade R-CNN: High Quality Object Detection and Instance Segmentation

Cai

Vasconcelos

2021

IEEE Trans. Pattern Anal. Mach. Intell.

990

616

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In object detection, the intersection over union (IoU) threshold is frequently used to define positives/negatives. The threshold used to train a detector defines its quality. While the commonly used threshold of 0.5 leads to noisy (low-quality) detections, detection performance frequently degrades for larger thresholds. This paradox of high-quality detection has two causes: 1) overfitting, due to vanishing positive samples for large thresholds, and 2) inference-time quality mismatch between detector and test hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, composed of a sequence of detectors trained with increasing IoU thresholds, is proposed to address these problems. The detectors are trained sequentially, using the output of a detector as training set for the next. This resampling progressively improves hypotheses quality, guaranteeing a positive training set of equivalent size for all detectors and minimizing overfitting. The same cascade is applied at inference, to eliminate quality mismatches between hypotheses and detectors. An implementation of the Cascade R-CNN without bells or whistles achieves state-of-the-art performance on the COCO dataset, and significantly improves high-quality detection on generic and specific object detection datasets, including VOC, KITTI, CityPerson, and WiderFace. Finally, the Cascade R-CNN is generalized to instance segmentation, with nontrivial improvements over the Mask R-CNN. To facilitate future research, two implementations are made available at https://github.com/zhaoweicai/cascade-rcnn (Caffe) and https://github.com/zhaoweicai/Detectron-Cascade-RCNN (Detectron). person: 1.00 person: 1.00 person: 0.99 person: 0.99 person: 0.87 person: 0.82 person: 0.77 person: 0.70 person: 0.64 person: 0.63 person: 0.56 frisbee: 1.00 frisbee: 1.00 frisbee: 0.99 frisbee: 0.97 (a) Detection of u = 0.5 person: 1.00 person: 0.99 person: 0.96 person: 0.94 person: 0.55 frisbee: 0.99 frisbee: 0.99 frisbee: 0.99 frisbee: 0.93

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“…In their models, a lightweight detector first rejects the majority easy negatives and feeds hard proposals to train detectors in next stage. For deep learning based detection algorithms, Yang et al [153] proposed CRAFT (Cascade Region-proposal-network And FasT-rcnn) which learned RPN and region classifiers with a cascaded learning strategy.…”

Section: Cascade Learningmentioning

confidence: 99%

Recent advances in deep learning for object detection

2020

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Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades.Visual object detection aims to find objects of certain target classes with precise localization in a given image and assign each object instance a corresponding class label. Due to the tremendous successes of deep learning based image classification, object detection techniques using deep learning have been actively studied in recent years. In this paper, we give a comprehensive survey of recent advances in visual object detection with deep learning. By reviewing a large body of recent related work in literature, we systematically analyze the existing object detection frameworks and organize the survey into three major parts: (i) detection components, (ii) learning strategies, and (iii) applications & benchmarks. In the survey, we cover a variety of factors affecting the detection performance in detail, such as detector architectures, feature learning, proposal generation, sampling strategies, etc.Finally, we discuss several future directions to facilitate and spur future research for visual object detection with deep learning.

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CRAFT Objects from Images

Cited by 112 publications

References 40 publications

Cascade R-CNN: Delving Into High Quality Object Detection

Cascade R-CNN: Delving Into High Quality Object Detection

Cascade R-CNN: High Quality Object Detection and Instance Segmentation

Recent advances in deep learning for object detection

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