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In this paper, we propose a hybrid object detection technique for incorporating the strengths of both approaches.
Open accessBook ChapterDOI
Ramazan Gokberk Cinbis, Stan Sclaroff 
07 Oct 2012
47 Citations
We propose a new model for object detection that is based on set representations of the contextual elements.
We show that our method can be used in conjunction with an object detection algorithm to reduce the rate of false positives.
The experimental results show that the proposed approaches are efficient in object detection.
Open accessProceedings ArticleDOI
01 Dec 2010
34 Citations
The results show that both the object-detection as well as the object-segmentation method are successful and outperform existing methods.
The experimental results show that the proposed method is superior to the state-of-the-art object detection model in terms of accuracy and false positive rate.

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What is the latest algorithm deep learning for object detection?3 answersThe latest algorithm for deep learning object detection is the Single Shot Detector (SSD) algorithm. SSD is an improved detection algorithm that uses the lightweight network MobileNet as the basic network in the SSD model to reduce computation time and extract image features efficiently. It establishes correlations between frames and achieves good performance in terms of detection accuracy and speed. Other popular algorithms for object detection include Faster R-CNN, HOG, R-CNN, R-FCN, SPP-net, and YOLO versions. These algorithms have been widely studied and compared through sample experiments. Real-time object detection using deep learning has become a burgeoning field, with CNNs being trained on large-scale image datasets to extract discriminative features effectively. Faster CNN architectures like SSD and YOLO, along with hardware acceleration strategies, have been proposed to improve real-time performance.
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How do you create a custom dataset for object detection?7 answers

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