scholarly journals R-YOLO: A Real-Time Text Detector for Natural Scenes with Arbitrary Rotation

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 888
Author(s):  
Xiqi Wang ◽  
Shunyi Zheng ◽  
Ce Zhang ◽  
Rui Li ◽  
Li Gui
Keyword(s):  
Real Time ◽  
Detection Efficiency ◽  
Input Image ◽  
Text Detection ◽  
Detection Methods ◽  
Natural Image ◽  
Natural Scenes ◽  
Novel Method ◽  
Bounding Boxes ◽  
Image Streams

Accurate and efficient text detection in natural scenes is a fundamental yet challenging task in computer vision, especially when dealing with arbitrarily-oriented texts. Most contemporary text detection methods are designed to identify horizontal or approximately horizontal text, which cannot satisfy practical detection requirements for various real-world images such as image streams or videos. To address this lacuna, we propose a novel method called Rotational You Only Look Once (R-YOLO), a robust real-time convolutional neural network (CNN) model to detect arbitrarily-oriented texts in natural image scenes. First, a rotated anchor box with angle information is used as the text bounding box over various orientations. Second, features of various scales are extracted from the input image to determine the probability, confidence, and inclined bounding boxes of the text. Finally, Rotational Distance Intersection over Union Non-Maximum Suppression is used to eliminate redundancy and acquire detection results with the highest accuracy. Experiments on benchmark comparison are conducted upon four popular datasets, i.e., ICDAR2015, ICDAR2013, MSRA-TD500, and ICDAR2017-MLT. The results indicate that the proposed R-YOLO method significantly outperforms state-of-the-art methods in terms of detection efficiency while maintaining high accuracy; for example, the proposed R-YOLO method achieves an F-measure of 82.3% at 62.5 fps with 720 p resolution on the ICDAR2015 dataset.

scholarly journals A generalised approach for high-throughput instance segmentation of stomata in microscope images

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Hiranya Jayakody ◽  
Paul Petrie ◽  
Hugo Jan de Boer ◽  
Mark Whitty
Keyword(s):  
High Throughput ◽  
Imaging Techniques ◽  
Detection Algorithm ◽  
Input Image ◽  
Detection Methods ◽  
Sample Collection ◽  
High Throughput Analysis ◽  
General Applicability ◽  
Additional Image ◽  
Bounding Boxes

Abstract Background Stomata analysis using microscope imagery provides important insight into plant physiology, health and the surrounding environmental conditions. Plant scientists are now able to conduct automated high-throughput analysis of stomata in microscope data, however, existing detection methods are sensitive to the appearance of stomata in the training images, thereby limiting general applicability. In addition, existing methods only generate bounding-boxes around detected stomata, which require users to implement additional image processing steps to study stomata morphology. In this paper, we develop a fully automated, robust stomata detection algorithm which can also identify individual stomata boundaries regardless of the plant species, sample collection method, imaging technique and magnification level. Results The proposed solution consists of three stages. First, the input image is pre-processed to remove any colour space biases occurring from different sample collection and imaging techniques. Then, a Mask R-CNN is applied to estimate individual stomata boundaries. The feature pyramid network embedded in the Mask R-CNN is utilised to identify stomata at different scales. Finally, a statistical filter is implemented at the Mask R-CNN output to reduce the number of false positive generated by the network. The algorithm was tested using 16 datasets from 12 sources, containing over 60,000 stomata. For the first time in this domain, the proposed solution was tested against 7 microscope datasets never seen by the algorithm to show the generalisability of the solution. Results indicated that the proposed approach can detect stomata with a precision, recall, and F-score of 95.10%, 83.34%, and 88.61%, respectively. A separate test conducted by comparing estimated stomata boundary values with manually measured data showed that the proposed method has an IoU score of 0.70; a 7% improvement over the bounding-box approach. Conclusions The proposed method shows robust performance across multiple microscope image datasets of different quality and scale. This generalised stomata detection algorithm allows plant scientists to conduct stomata analysis whilst eliminating the need to re-label and re-train for each new dataset. The open-source code shared with this project can be directly deployed in Google Colab or any other Tensorflow environment.

scholarly journals Real Time In-situ Detection System Research for Methane in Offshore Shallow Gas based on Thin Film Interface

2018 ◽  
Vol 232 ◽  
pp. 04053
Author(s):  
Cheng-xing Miao ◽  
Qing Li ◽  
Sheng-yao Jia
Keyword(s):  
Real Time ◽  
Detection Efficiency ◽  
Detection System ◽  
Detection Methods ◽  
Shallow Gas ◽  
Micro Gas Sensor ◽  
Detection Probe ◽  
On Line ◽  
In Situ Detection

In order to get ridded of the non real-time detection methods of artificial site sampled and laboratory instrument analyzed in the field of methane detection in the offshore shallow gas, real-time in-situ detection system for methane in offshore shallow gas was designed by the film interface.The methane in the offshore shallow gas through the gas-liquid separation membrane of polymer permeation into the system internal detection probe, analog infrared micro gas sensor sensed the methane concentration and the corresponded output value, data acquisition and communication node fitted into standard gas concentration.Based on the experimental data compared with the traditional detection method, and further analyzed the causes of error produced by the case experiment. The application results show that the system can achieve a single borehole layout, long-term on-line in-situ on-line detection, and improve the detection efficiency and the timeliness of the detection data.

scholarly journals A Generalised Approach for High-throughput Instance Segmentation of Stomata in Microscope Images

2020 ◽  
Author(s):  
Hiranya Samanga Jayakody ◽  
Paul Petrie ◽  
Hugo de Boer ◽  
Mark Whitty
Keyword(s):  
High Throughput ◽  
Imaging Techniques ◽  
Detection Algorithm ◽  
Input Image ◽  
Detection Methods ◽  
Sample Collection ◽  
High Throughput Analysis ◽  
General Applicability ◽  
Additional Image ◽  
Bounding Boxes

Abstract Background: Stomata analysis using microscope imagery provides important insight into plant physiology, health and the surrounding environmental conditions. Plant scientists are now able to conduct automated high-throughput analysis of stomata in microscope data, however, existing detection methods are sensitive to the appearance of stomata in the training images, thereby limiting general applicability. In addition, existing methods only generate bounding-boxes around detected stomata, which require users to implement additional image processing steps to study stomata morphology. In this paper, we develop a fully automated, robust stomata detection algorithm which can also identify individual stomata boundaries regardless of the plant species, sample collection method, imaging technique and magnification level. Results: The proposed solution consists of three stages. Firstly, the input image is pre-processed to remove any colour-space biases occurring from different sample collection and imaging techniques. Secondly, a Mask R-CNN is applied to estimate individual stomata boundaries and the feature pyramid network embedded in the Mask R-CNN allows the network to identify stomata at different scales. Finally, a statistical filter is implemented at the Mask R-CNN output to reduce the number of false positive generated by the network. The algorithm was tested using 16 datasets from 12 sources, containing over 60,000 stomata. For the first time in this domain, the proposed solution was tested against 7 microscope datasets never seen by the algorithm to show the generalisability of the solution. Results indicated that the proposed approach can detect stomata with a precision, recall, and F-score of 95.10%, 83.34%, and 88.61%, respectively. A separate test conducted by comparing estimated stomata boundary values with manually measured data showed that the proposed method has an IoU score of 0.70; a 7% improvement over the bounding-box approach. Conclusion: The proposed method shows robust performance across multiple microscope image datasets of different quality and scale. This generalised stomata detection algorithm allows plant scientists to conduct stomata analysis whilst eliminating the need to re-label and re-train for each new dataset. The open-source code for the project can be directly deployed in Google Colab or any other Tensorflow environment.

scholarly journals Scene Text Detection with Supervised Pyramid Context Network

2019 ◽  
Vol 33 ◽  
pp. 9038-9045 ◽  
Author(s):  
Enze Xie ◽  
Yuhang Zang ◽  
Shuai Shao ◽  
Gang Yu ◽  
Cong Yao ◽  
...  
Keyword(s):  
Semantic Information ◽  
State Of The Art ◽  
Text Detection ◽  
Detection Methods ◽  
Natural Scenes ◽  
The Past ◽  
Scene Text Detection ◽  
Scene Text ◽  
Previous State ◽  
Feature Pyramid

Scene text detection methods based on deep learning have achieved remarkable results over the past years. However, due to the high diversity and complexity of natural scenes, previous state-of-the-art text detection methods may still produce a considerable amount of false positives, when applied to images captured in real-world environments. To tackle this issue, mainly inspired by Mask R-CNN, we propose in this paper an effective model for scene text detection, which is based on Feature Pyramid Network (FPN) and instance segmentation. We propose a supervised pyramid context network (SPCNET) to precisely locate text regions while suppressing false positives.Benefited from the guidance of semantic information and sharing FPN, SPCNET obtains significantly enhanced performance while introducing marginal extra computation. Experiments on standard datasets demonstrate that our SPCNET clearly outperforms start-of-the-art methods. Specifically, it achieves an F-measure of 92.1% on ICDAR2013, 87.2% on ICDAR2015, 74.1% on ICDAR2017 MLT and 82.9% on

Detection of Campylobacter jejuni from fresh produce: comparison of culture- and PCR-based techniques, and metagenomic approach for analyses of the microbiome before and after enrichment

2021 ◽  
Author(s):  
Jung-Whan Chon ◽  
Ji Young Jung ◽  
Youngbeom Ahn ◽  
Dongryeoul Bae ◽  
Saeed Khan ◽  
...  
Keyword(s):  
Real Time ◽  
Campylobacter Jejuni ◽  
Membrane Filtration ◽  
Detection Efficiency ◽  
Fresh Produce ◽  
Culture Method ◽  
Detection Methods ◽  
Filtration Method ◽  
Significant Difference ◽  
Alfalfa Sprouts

In this study, we compared the efficiency of culture-based methods with or without membrane filtration, real-time PCR and digital droplet PCR (ddPCR) for the detection of Campylobacter in fresh produce. Alfalfa sprouts, clover sprouts, coleslaw, and lettuce salad spiked with Campylobacter jejuni ( C. jejuni ) were enriched in Bolton broth for 48 h and enrichment cultures were either directly inoculated onto modified charcoal-cefoperazone-deoxycholate agar or applied on membrane filters placed on the surface of plating media. In parallel, 2 mL Bolton broth cultures were taken to extract DNA for real-time and ddPCR assays and bacterial community analysis. There was no significant difference ( p > 0.05) in the detection efficiency of positive Campylobacter isolates from coleslaw and lettuce salad using four detection methods. However, for sprout samples, the detection efficiency of the culture method was significantly ( p < 0.05) lower than those of the two PCR assays and the filtration method. The analysis also revealed the presence of Pseudomonas and Acinetobacter as the most prevalent competing microbiota in enriched culture and only Acinetobacter on agar plates in the selective culture step.

scholarly journals Real-Time Water Surface Object Detection Based on Improved Faster R-CNN

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3523 ◽  
Author(s):  
Lili Zhang ◽  
Yi Zhang ◽  
Zhen Zhang ◽  
Jie Shen ◽  
Huibin Wang
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Water Surface ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Natural Scenes ◽  
The North ◽  
Aspect Ratios ◽  
High Level ◽  
Surface Object

In this paper, we consider water surface object detection in natural scenes. Generally, background subtraction and image segmentation are the classical object detection methods. The former is highly susceptible to variable scenes, so its accuracy will be greatly reduced when detecting water surface objects due to the changing of the sunlight and waves. The latter is more sensitive to the selection of object features, which will lead to poor generalization as a result, so it cannot be applied widely. Consequently, methods based on deep learning have recently been proposed. The River Chief System has been implemented in China recently, and one of the important requirements is to detect and deal with the water surface floats in a timely fashion. In response to this case, we propose a real-time water surface object detection method in this paper which is based on the Faster R-CNN. The proposed network model includes two modules and integrates low-level features with high-level features to improve detection accuracy. Moreover, we propose to set the different scales and aspect ratios of anchors by analyzing the distribution of object scales in our dataset, so our method has good robustness and high detection accuracy for multi-scale objects in complex natural scenes. We utilized the proposed method to detect the floats on the water surface via a three-day video surveillance stream of the North Canal in Beijing, and validated its performance. The experiments show that the mean average precision (MAP) of the proposed method was 83.7%, and the detection speed was 13 frames per second. Therefore, our method can be applied in complex natural scenes and mostly meets the requirements of accuracy and speed of water surface object detection online.

scholarly journals SEMPANet: A Modified Path Aggregation Network with Squeeze-Excitation for Scene Text Detection

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2657
Author(s):  
Shuangshuang Li ◽  
Wenming Cao
Keyword(s):  
Feature Extraction ◽  
Early Stage ◽  
Text Detection ◽  
Detection Methods ◽  
Good Effect ◽  
Natural Scenes ◽  
Training Time ◽  
Scene Text Detection ◽  
Scene Text ◽  
Curved Text

Recently, various object detection frameworks have been applied to text detection tasks and have achieved good performance in the final detection. With the further expansion of text detection application scenarios, the research value of text detection topics has gradually increased. Text detection in natural scenes is more challenging for horizontal text based on a quadrilateral detection box and for curved text of any shape. Most networks have a good effect on the balancing of target samples in text detection, but it is challenging to deal with small targets and solve extremely unbalanced data. We continued to use PSENet to deal with such problems in this work. On the other hand, we studied the problem that most of the existing scene text detection methods use ResNet and FPN as the backbone of feature extraction, and improved the ResNet and FPN network parts of PSENet to make it more conducive to the combination of feature extraction in the early stage. A SEMPANet framework without an anchor and in one stage is proposed to implement a lightweight model, which is embodied in the training time of about 24 h. Finally, we selected the two most representative datasets for oriented text and curved text to conduct experiments. On ICDAR2015, the improved network’s latest results further verify its effectiveness; it reached 1.01% in F-measure compared with PSENet-1s. On CTW1500, the improved network performed better than the original network on average.

scholarly journals A Generalised Approach for High-throughput Instance Segmentation of Stomata in Microscope Images

2020 ◽  
Author(s):  
Hiranya Samanga Jayakody ◽  
Paul Petrie ◽  
Hugo de Boer ◽  
Mark Whitty
Keyword(s):  
High Throughput ◽  
Imaging Techniques ◽  
Detection Algorithm ◽  
Input Image ◽  
Detection Methods ◽  
Sample Collection ◽  
High Throughput Analysis ◽  
General Applicability ◽  
Additional Image ◽  
Bounding Boxes

Abstract Background: Stomata analysis using microscope imagery provides important insight into plant physiology, health and the surrounding environmental conditions. Plant scientists are now able to conduct automated high-throughput analysis of stomata in microscope data, however, existing detection methods are sensitive to the appearance of stomata in the training images, thereby limiting general applicability. In addition, existing methods only generate bounding-boxes around detected stomata, which require users to implement additional image processing steps to study stomata morphology. In this paper, we develop a fully automated, robust stomata detection algorithm which can also identify individual stomata boundaries regardless of the plant species, sample collection method, imaging technique and magnification level. Results: The proposed solution consists of three stages. First, the input image is pre-processed to remove any colour space biases occurring from different sample collection and imaging techniques. Then, a Mask R-CNN is applied to estimate individual stomata boundaries. The feature pyramid network embedded in the Mask R-CNN is utilised to identify stomata at different scales. Finally, a statistical filter is implemented at the Mask R-CNN output to reduce the number of false positive generated by the network. The algorithm was tested using 16 datasets from 12 sources, containing over 60,000 stomata. For the first time in this domain, the proposed solution was tested against 7 microscope datasets never seen by the algorithm to show the generalisability of the solution. Results indicated that the proposed approach can detect stomata with a precision, recall, and F-score of 95.10\%, 83.34\%, and 88.61\%, respectively. A separate test conducted by comparing estimated stomata boundary values with manually measured data showed that the proposed method has an IoU score of 0.70; a 7\% improvement over the bounding-box approach. Conclusions: The proposed method shows robust performance across multiple microscope image datasets of different quality and scale. This generalised stomata detection algorithm allows plant scientists to conduct stomata analysis whilst eliminating the need to re-label and re-train for each new dataset. The open-source code shared with this project can be directly deployed in Google Colab or any other Tensorflow environment.

scholarly journals NSD-SSD: A Novel Real-Time Ship Detector Based on Convolutional Neural Network in Surveillance Video

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jiuwu Sun ◽  
Zhijing Xu ◽  
Shanshan Liang
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Detection Efficiency ◽  
Rapid Development ◽  
Outdoor Environment ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Single Shot ◽  
Ship Detection ◽  
Real Time Detection

With the rapid development of the marine industry, intelligent ship detection plays a very important role in the marine traffic safety and the port management. Current detection methods mainly focus on synthetic aperture radar (SAR) images, which is of great significance to the field of ship detection. However, these methods sometimes cannot meet the real-time requirement. To solve the problems, a novel ship detection network based on SSD (Single Shot Detector), named NSD-SSD, is proposed in this paper. Nowadays, the surveillance system is widely used in the indoor and outdoor environment, and its combination with deep learning greatly promotes the development of intelligent object detection and recognition. The NSD-SSD uses visual images captured by surveillance cameras to achieve real-time detection and further improves detection performance. First, dilated convolution and multiscale feature fusion are combined to improve the small objects’ performance and detection accuracy. Second, an improved prediction module is introduced to enhance deeper feature extraction ability of the model, and the mean Average Precision (mAP) and recall are significant improved. Finally, the prior boxes are reconstructed by using the K-means clustering algorithm, the Intersection-over-Union (IoU) is higher, and the visual effect is better. The experimental results based on ship images show that the mAP and recall can reach 89.3% and 93.6%, respectively, which outperforms the representative model (Faster R-CNN, SSD, and YOLOv3). Moreover, our model’s FPS is 45, which can meet real-time detection acquirement well. Hence, the proposed method has the better overall performance and achieves higher detection efficiency and better robustness.

scholarly journals Real-time on-board pedestrian detection using generic single-stage algorithms and on-road databases

2020 ◽  
Vol 17 (5) ◽  
pp. 172988142092917
Author(s):  
Vicent Ortiz Castelló ◽  
Omar del Tejo Catalá ◽  
Ismael Salvador Igual ◽  
Juan-Carlos Perez-Cortes
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Pedestrian Detection ◽  
Input Image ◽  
Performance Comparison ◽  
Detection Methods ◽  
Computational Time ◽  
Image Size ◽  
Single Class ◽  
Real Time System

Pedestrian detection is a particular case of object detection that helps to reduce accidents in advanced driver-assistance systems and autonomous vehicles. It is not an easy task because of the variability of the objects and the time constraints. A performance comparison of object detection methods, including both GPU and non-GPU implementations over a variety of on-road specific databases, is provided. Computer vision multi-class object detection can be integrated on sensor fusion modules where recall is preferred over precision. For this reason, ad hoc training with a single class for pedestrians has been performed and we achieved a significant increase in recall. Experiments have been carried out on several architectures and a special effort has been devoted to achieve a feasible computational time for a real-time system. Finally, an analysis of the input image size allows to fine-tune the model and get better results with practical costs.

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