Open Access
Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector
Р Ю Чуйков,Д А Юдин +1 more
- Vol. 2, Iss: 4
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The article was published on 2017-01-01 and is currently open access. It has received 1687 citations till now.read more
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Enhancing aphid detection framework based on ORB and convolutional neural networks
TL;DR: Experiments indicate that this framework based on oriented FAST and rotated BRIEF and CNNs and EADF to detect aphids in images has higher accuracy than state-of-the-art two-stage methods and demonstrates its competency.
Journal ArticleDOI
Dynamic Label Assignment for Object Detection by Combining Predicted IoUs and Anchor IoUs
TL;DR: This paper introduces a simple while effective approach to perform label assignment dynamically based on the training status with predictions, showing improvements in the performance of the detection models with the adaptive label assignment algorithm and lower bounding box losses for those positive samples, indicating more samples with higher-quality predicted boxes are selected as positives.
Journal ArticleDOI
Research on Insulator Defect Detection Based on an Improved MobilenetV1-YOLOv4
TL;DR: In this paper , the authors proposed an improved MobilenetV1-YOLOv4 model to improve the detection speed and efficiency of traditional insulator defect detection algorithms.
Journal ArticleDOI
An Object Detection Model for Paint Surface Detection Based on Improved YOLOv3
TL;DR: This work proposes a defect detection method based on the improved YOLOv3 algorithm that realizes the real-time detection of the paint surface defects of the five-star feet of the office chair very well.
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Breast lesion detection using an anchor-free network from ultrasound images with segmentation-based enhancement
Yu-Fen Wang,Yudong Yao +1 more
TL;DR: Wang et al. as mentioned in this paper proposed a deep learning based breast ultrasound detection system to assist doctors in the diagnosis of breast cancer, which consists of two steps: 1) Contrast enhancement of breast ultrasound images using segmentation-based enhancement methods.
References
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Proceedings ArticleDOI
Feature Pyramid Networks for Object Detection
TL;DR: This paper exploits the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost and achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles.
Proceedings ArticleDOI
Focal Loss for Dense Object Detection
TL;DR: This paper proposes to address the extreme foreground-background class imbalance encountered during training of dense detectors by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples, and develops a novel Focal Loss, which focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.
Journal ArticleDOI
SECOND: Sparsely Embedded Convolutional Detection
Yan Yan,Yuxing Mao,Bo Li +2 more
TL;DR: An improved sparse convolution method for Voxel-based 3D convolutional networks is investigated, which significantly increases the speed of both training and inference and introduces a new form of angle loss regression to improve the orientation estimation performance.
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A State-of-the-Art Survey on Deep Learning Theory and Architectures
Zahangir Alom,Tarek M. Taha,Chris Yakopcic,Stefan Westberg,Paheding Sidike,Mst Shamima Nasrin,Mahmudul Hasan,Brian Van Essen,Abdul A. S. Awwal,Vijayan K. Asari +9 more
TL;DR: This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network and goes on to cover Convolutional Neural Network, Recurrent Neural Network (RNN), and Deep Reinforcement Learning (DRL).
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A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition
TL;DR: A deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions, and combines each of these meta-architectures with “deep feature extractors” such as VGG net and Residual Network.