SE-Mask R-CNN: An improved Mask R-CNN for apple detection and segmentation

2021 ◽  
pp. 1-11
Author(s):  
Yikun Liu ◽  
Gongping Yang ◽  
Yuwen Huang ◽  
Yilong Yin
Keyword(s):  
Aspect Ratio ◽  
State Of The Art ◽  
Correct Detection ◽  
Experimental Result ◽  
Complex Environment ◽  
Yield Estimation ◽  
Informative Feature ◽  
Bounding Boxes ◽  
Computational Resources ◽  
Speed And Accuracy

Fruit detection and segmentation is an essential operation of orchard yield estimation, the result of yield estimation directly depends on the speed and accuracy of detection and segmentation. In this work, we propose an effective method based on Mask R-CNN to detect and segment apples under complex environment of orchard. Firstly, the squeeze-and-excitation block is introduced into the ResNet-50 backbone, which can distribute the available computational resources to the most informative feature map in channel-wise. Secondly, the aspect ratio is introduced into the bounding box regression loss, which can promote the regression of bounding boxes by deforming the shape of bounding boxes to the apple boxes. Finally, we replace the NMS operation in Mask R-CNN by Soft-NMS, which can remove the redundant bounding boxes and obtain the correct detection results reasonably. The experimental result on the Minneapple dataset demonstrates that our method overperform several state-of-the-art on apple detection and segmentation.

scholarly journals A Sign of Things to Come: Predicting the Perception of Above-the-Fold Time in Web Browsing

2021 ◽  
Vol 13 (2) ◽  
pp. 50
Author(s):  
Hamed Z. Jahromi ◽  
Declan Delaney ◽  
Andrew Hines
Keyword(s):  
Web Application ◽  
Web Applications ◽  
State Of The Art ◽  
Influencing Factor ◽  
The State ◽  
Experimental Result ◽  
Web Page ◽  
To Come ◽  
Web Quality ◽  
The Web

Content is a key influencing factor in Web Quality of Experience (QoE) estimation. A web user’s satisfaction can be influenced by how long it takes to render and visualize the visible parts of the web page in the browser. This is referred to as the Above-the-fold (ATF) time. SpeedIndex (SI) has been widely used to estimate perceived web page loading speed of ATF content and a proxy metric for Web QoE estimation. Web application developers have been actively introducing innovative interactive features, such as animated and multimedia content, aiming to capture the users’ attention and improve the functionality and utility of the web applications. However, the literature shows that, for the websites with animated content, the estimated ATF time using the state-of-the-art metrics may not accurately match completed ATF time as perceived by users. This study introduces a new metric, Plausibly Complete Time (PCT), that estimates ATF time for a user’s perception of websites with and without animations. PCT can be integrated with SI and web QoE models. The accuracy of the proposed metric is evaluated based on two publicly available datasets. The proposed metric holds a high positive Spearman’s correlation (rs=0.89) with the Perceived ATF reported by the users for websites with and without animated content. This study demonstrates that using PCT as a KPI in QoE estimation models can improve the robustness of QoE estimation in comparison to using the state-of-the-art ATF time metric. Furthermore, experimental result showed that the estimation of SI using PCT improves the robustness of SI for websites with animated content. The PCT estimation allows web application designers to identify where poor design has significantly increased ATF time and refactor their implementation before it impacts end-user experience.

scholarly journals An Anchor-Free Siamese Network with Multi-Template Update for Object Tracking

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1067
Author(s):  
Tongtong Yuan ◽  
Wenzhu Yang ◽  
Qian Li ◽  
Yuxia Wang
Keyword(s):  
Object Tracking ◽  
Correlation Energy ◽  
Feature Maps ◽  
Siamese Network ◽  
Template Update ◽  
Free Network ◽  
Multiple Prediction ◽  
Bounding Boxes ◽  
High Level ◽  
Speed And Accuracy

Siamese trackers are widely used in various fields for their advantages of balancing speed and accuracy. Compared with the anchor-based method, the anchor-free-based approach can reach faster speeds without any drop in precision. Inspired by the Siamese network and anchor-free idea, an anchor-free Siamese network (AFSN) with multi-template updates for object tracking is proposed. To improve tracking performance, a dual-fusion method is adopted in which the multi-layer features and multiple prediction results are combined respectively. The low-level feature maps are concatenated with the high-level feature maps to make full use of both spatial and semantic information. To make the results as stable as possible, the final results are obtained by combining multiple prediction results. Aiming at the template update, a high-confidence multi-template update mechanism is used. The average peak to correlation energy is used to determine whether the template should be updated. We use the anchor-free network to implement object tracking in a per-pixel manner, which computes the object category and bounding boxes directly. Experimental results indicate that the average overlap and success rate of the proposed algorithm increase by about 5% and 10%, respectively, compared to the SiamRPN++ algorithm when running on the dataset of GOT-10k (Generic Object Tracking Benchmark).

scholarly journals Proximity Effect Aware Detailed Placement in Electron Beam Lithography

2018 ◽  
Vol 232 ◽  
pp. 04046
Author(s):  
Yuhang Chen ◽  
Zhipeng Huang ◽  
Xiongfeng Chen ◽  
Jianli Chen ◽  
Wenxing Zhu
Keyword(s):  
Electron Beam ◽  
Objective Function ◽  
Proximity Effect ◽  
Electron Beam Lithography ◽  
State Of The Art ◽  
Experimental Result ◽  
Accurate Evaluation ◽  
Evaluation Scheme ◽  
Gauss Transform ◽  
Detailed Placement

Proximity effect is one of the most tremendous consequences that produces unacceptable exposures during electron beam lithography (EBL), and thus distorting the layout pattern. In this paper, we propose the first work which considers the proximity effect during layout stage. We first give an accurate evaluation scheme to estimate the proximity effect by fast Gauss transform. Then, we devote a proximity effect aware detailed placement objective function to simultaneously consider wirelength, density and proximity effect. Furthermore, cell swapping and cell matching based methods are used to optimize the objective function such that there is no overlap among cells. Compared with a state-of-the-art work, experimental result shows that our algorithm can efficiently reduce the proximity variations and maintain high wirelength quality at a reasonable runtime.

FAST RRT* 3D-Sliced Planner for Autonomous Exploration Using MAVs

2021 ◽  
pp. 1-12
Author(s):  
Á. Martínez Novo ◽  
Liang Lu ◽  
Pascual Campoy
Keyword(s):  
State Of The Art ◽  
Micro Aerial Vehicles ◽  
Autonomous Exploration ◽  
Signed Distance ◽  
Aerial Vehicles ◽  
3D Environment ◽  
Frontier Points ◽  
Computational Resources ◽  
Next Best View ◽  
Signed Distance Field

This paper addresses the challenge to build an autonomous exploration system using Micro-Aerial Vehicles (MAVs). MAVs are capable of flying autonomously, generating collision-free paths to navigate in unknown areas and also reconstructing the environment at which they are deployed. One of the contributions of our system is the “3D-Sliced Planner” for exploration. The main innovation is the low computational resources needed. This is because Optimal-Frontier-Points (OFP) to explore are computed in 2D slices of the 3D environment using a global Rapidly-exploring Random Tree (RRT) frontier detector. Then, the MAV can plan path routes to these points to explore the surroundings with our new proposed local “FAST RRT* Planner” that uses a tree reconnection algorithm based on cost, and a collision checking algorithm based on Signed Distance Field (SDF). The results show the proposed explorer takes 43.95% less time to compute exploration points and paths when compared with the State-of-the-Art represented by the Receding Horizon Next Best View Planner (RH-NBVP) in Gazebo simulations.

Accelerating Mobile-Cloud Computing

2015 ◽  
pp. 1933-1955
Author(s):  
Tolga Soyata ◽  
He Ba ◽  
Wendi Heinzelman ◽  
Minseok Kwon ◽  
Jiye Shi
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
High Speed ◽  
Mobile Cloud Computing ◽  
Response Times ◽  
State Of The Art ◽  
The State ◽  
Mobile Cloud ◽  
Computational Resources ◽  
Traditional Approaches

With the recent advances in cloud computing and the capabilities of mobile devices, the state-of-the-art of mobile computing is at an inflection point, where compute-intensive applications can now run on today's mobile devices with limited computational capabilities. This is achieved by using the communications capabilities of mobile devices to establish high-speed connections to vast computational resources located in the cloud. While the execution scheme based on this mobile-cloud collaboration opens the door to many applications that can tolerate response times on the order of seconds and minutes, it proves to be an inadequate platform for running applications demanding real-time response within a fraction of a second. In this chapter, the authors describe the state-of-the-art in mobile-cloud computing as well as the challenges faced by traditional approaches in terms of their latency and energy efficiency. They also introduce the use of cloudlets as an approach for extending the utility of mobile-cloud computing by providing compute and storage resources accessible at the edge of the network, both for end processing of applications as well as for managing the distribution of applications to other distributed compute resources.

scholarly journals A Scene Text Detector for Text with Arbitrary Shapes

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Weijia Wu ◽  
Jici Xing ◽  
Cheng Yang ◽  
Yuxing Wang ◽  
Hong Zhou
Keyword(s):  
State Of The Art ◽  
Recognition Task ◽  
Text Detection ◽  
Supplementary Information ◽  
Complex Environment ◽  
Irregular Shapes ◽  
Scene Text Detection ◽  
Scene Text ◽  
F Measure ◽  
Instance Segmentation

The performance of text detection is crucial for the subsequent recognition task. Currently, the accuracy of the text detector still needs further improvement, particularly those with irregular shapes in a complex environment. We propose a pixel-wise method based on instance segmentation for scene text detection. Specifically, a text instance is split into five components: a Text Skeleton and four Directional Pixel Regions, then restoring itself based on these elements and receiving supplementary information from other areas when one fails. Besides, a Confidence Scoring Mechanism is designed to filter characters similar to text instances. Experiments on several challenging benchmarks demonstrate that our method achieves state-of-the-art results in scene text detection with an F-measure of 84.6% on Total-Text and 86.3% on CTW1500.

scholarly journals Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization

2020 ◽  
Vol 10 (24) ◽  
pp. 8961
Author(s):  
Peng-Yeng Yin ◽  
Po-Yen Chen ◽  
Ying-Chieh Wei ◽  
Rong-Fuh Day
Keyword(s):  
Global Optimization ◽  
Firefly Algorithm ◽  
State Of The Art ◽  
Pattern Search ◽  
Experimental Result ◽  
Adaptive Memory ◽  
Glowworm Swarm Optimization ◽  
Metaheuristic Methods ◽  
Swarming Behavior ◽  
Better Than

Recently, two evolutionary algorithms (EAs), the glowworm swarm optimization (GSO) and the firefly algorithm (FA), have been proposed. The two algorithms were inspired by the bioluminescence process that enables the light-mediated swarming behavior for mating or foraging. From our literature survey, we are convinced with much evidence that the EAs can be more effective if appropriate responsive strategies contained in the adaptive memory programming (AMP) domain are considered in the execution. This paper contemplates this line and proposes the Cyber Firefly Algorithm (CFA), which integrates key elements of the GSO and the FA and further proliferates the advantages by featuring the AMP-responsive strategies including multiple guiding solutions, pattern search, multi-start search, swarm rebuilding, and the objective landscape analysis. The robustness of the CFA has been compared against the GSO, FA, and several state-of-the-art metaheuristic methods. The experimental result based on intensive statistical analyses showed that the CFA performs better than the other algorithms for global optimization of benchmark functions.

NEW EDGE CHARACTERISTICS FOR SCENE AND OBJECT CLASSIFICATION

2012 ◽  
Vol 26 (01) ◽  
pp. 1255001 ◽  
Author(s):  
PALAIAHNAKOTE SHIVAKUMARA ◽  
DEEPU RAJAN ◽  
SURESH ANAND SADANANTHAN
Keyword(s):  
Aspect Ratio ◽  
State Of The Art ◽  
Object Classification ◽  
The State ◽  
Complex Learning ◽  
Average Percentage ◽  
Class A ◽  
Training Samples ◽  
Learning Schemes ◽  
Class Representative

In this paper, we show that simple edge characteristics in images, when judiciously combined, can result in improved scene and object classification. Unlike existing methods that require a large number of training samples and complex learning schemes, our method discovers simple edge properties. We introduce three sets of edge properties, namely, centroid, compactness and aspect ratio of edges in the image. The combinations of these edge properties are used to discriminate among images in each class. A class representative is calculated for each class according to the average percentage of edges that satisfy the property of a particular class. This percentage for an unknown image is compared to the class representative to assign a label to it. It is shown that this simple edge properties-based method outperforms some of the state-of-the-art results on scene and object classification on standard databases.

scholarly journals Adaptive and Efficient Mixture-Based Representation for Range Data

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3272
Author(s):  
Minghe Cao ◽  
Jianzhong Wang ◽  
Li Ming
Keyword(s):  
State Of The Art ◽  
Gaussian Mixture ◽  
Range Data ◽  
Time Efficiency ◽  
Information Theoretic ◽  
Local Environments ◽  
The Hierarchical Structure ◽  
Data Points ◽  
Computational Resources ◽  
Research Domains

Modern range sensors generate millions of data points per second, making it difficult to utilize all incoming data effectively in real time for devices with limited computational resources. The Gaussian mixture model (GMM) is a convenient and essential tool commonly used in many research domains. In this paper, an environment representation approach based on the hierarchical GMM structure is proposed, which can be utilized to model environments with weighted Gaussians. The hierarchical structure accelerates training by recursively segmenting local environments into smaller clusters. By adopting the information-theoretic distance and shape of probabilistic distributions, weighted Gaussians can be dynamically allocated to local environments in an arbitrary scale, leading to a full adaptivity in the number of Gaussians. Evaluations are carried out in terms of time efficiency, reconstruction, and fidelity using datasets collected from different sensors. The results demonstrate that the proposed approach is superior with respect to time efficiency while maintaining the high fidelity as compared to other state-of-the-art approaches.

scholarly journals An Experiment on the Use of Genetic Algorithms for Topology Selection in Deep Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Fernando Mattioli ◽  
Daniel Caetano ◽  
Alexandre Cardoso ◽  
Eduardo Naves ◽  
Edgard Lamounier
Keyword(s):  
Genetic Algorithms ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Complex Task ◽  
Trial And Error ◽  
Complex Solution ◽  
Computation Algorithms ◽  
Computational Resources ◽  
Topology Selection

The choice of a good topology for a deep neural network is a complex task, essential for any deep learning project. This task normally demands knowledge from previous experience, as the higher amount of required computational resources makes trial and error approaches prohibitive. Evolutionary computation algorithms have shown success in many domains, by guiding the exploration of complex solution spaces in the direction of the best solutions, with minimal human intervention. In this sense, this work presents the use of genetic algorithms in deep neural networks topology selection. The evaluated algorithms were able to find competitive topologies while spending less computational resources when compared to state-of-the-art methods.

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