scholarly journals Improved YOLOv4-tiny network for real-time electronic component detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ce Guo ◽  
Xiao-ling Lv ◽  
Yan Zhang ◽  
Ming-lu Zhang
Keyword(s):  
Conveyor Belt ◽  
Electronics Industry ◽  
Electronic Component ◽  
Detection Accuracy ◽  
Electronic Components ◽  
Original Algorithm ◽  
Intelligent Robots ◽  
Industry Environment ◽  
Feature Information ◽  
High Level

AbstractIn the electronics industry environment, rapid recognition of objects to be grasped from digital images is essential for visual guidance of intelligent robots. However, electronic components have a small size, are difficult to distinguish, and are in motion on a conveyor belt, making target detection more difficult. For this reason, the YOLOv4-tiny method is used to detect electronic components and is improved. Then, different network structures are built for the adaptive integration of middle- and high-level features to address the phenomenon in which the original algorithm integrates all feature information indiscriminately. The method is deployed on an electronic component dataset for validation. Experimental results show that the accuracy of the original algorithm is improved from 93.74 to 98.6%. Compared with other current mainstream algorithms, such as Faster RCNN, SSD, RefineDet, EfficientDet, and YOLOv4, the method can maintain high detection accuracy at the fastest speed. The method can provide a technical reference for the development of manufacturing robots in the electronics industry.

Vivid Imagers Are Better at Detecting Salient Changes

2006 ◽  
Vol 27 (4) ◽  
pp. 218-228 ◽  
Author(s):  
Paul Rodway ◽  
Karen Gillies ◽  
Astrid Schepman
Keyword(s):  
Individual Differences ◽  
Change Detection ◽  
Visual Imagery ◽  
Detection Task ◽  
Level Of Detail ◽  
Detection Accuracy ◽  
Change Detection Task ◽  
Term Change ◽  
High Level

This study examined whether individual differences in the vividness of visual imagery influenced performance on a novel long-term change detection task. Participants were presented with a sequence of pictures, with each picture and its title displayed for 17  s, and then presented with changed or unchanged versions of those pictures and asked to detect whether the picture had been changed. Cuing the retrieval of the picture's image, by presenting the picture's title before the arrival of the changed picture, facilitated change detection accuracy. This suggests that the retrieval of the picture's representation immunizes it against overwriting by the arrival of the changed picture. The high and low vividness participants did not differ in overall levels of change detection accuracy. However, in replication of Gur and Hilgard (1975) , high vividness participants were significantly more accurate at detecting salient changes to pictures compared to low vividness participants. The results suggest that vivid images are not characterised by a high level of detail and that vivid imagery enhances memory for the salient aspects of a scene but not all of the details of a scene. Possible causes of this difference, and how they may lead to an understanding of individual differences in change detection, are considered.

scholarly journals A survey: which features are required for dynamic visual simultaneous localization and mapping?

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zewen Xu ◽  
Zheng Rong ◽  
Yihong Wu
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Dynamic Environments ◽  
Visual Features ◽  
Advantages And Disadvantages ◽  
Intelligent Robots ◽  
Localization And Mapping ◽  
High Level ◽  
Static World ◽  
Robotic Applications ◽  
Significant Attention

AbstractIn recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted significant attention from both academia and industry. Some pioneering work on this technique has expanded the potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments. Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of the advantages and disadvantages of different visual features is provided in this article.

scholarly journals A Research on Landslides Automatic Extraction Model Based on the Improved Mask R-CNN

2021 ◽  
Vol 10 (3) ◽  
pp. 168
Author(s):  
Peng Liu ◽  
Yongming Wei ◽  
Qinjun Wang ◽  
Jingjing Xie ◽  
Yu Chen ◽  
...  
Keyword(s):  
Remote Sensing Data ◽  
Extraction Methods ◽  
New Method ◽  
Detection Accuracy ◽  
Sufficient Information ◽  
Automatic Extraction ◽  
Emergency Rescue ◽  
Feature Pyramid ◽  
High Level ◽  
Extraction Model

Landslides are the most common and destructive secondary geological hazards caused by earthquakes. It is difficult to extract landslides automatically based on remote sensing data, which is import for the scenario of disaster emergency rescue. The literature review showed that the current landslides extraction methods mostly depend on expert interpretation which was low automation and thus was unable to provide sufficient information for earthquake rescue in time. To solve the above problem, an end-to-end improved Mask R-CNN model was proposed. The main innovations of this paper were (1) replacing the feature extraction layer with an effective ResNeXt module to extract the landslides. (2) Increasing the bottom-up channel in the feature pyramid network to make full use of low-level positioning and high-level semantic information. (3) Adding edge losses to the loss function to improve the accuracy of the landslide boundary detection accuracy. At the end of this paper, Jiuzhaigou County, Sichuan Province, was used as the study area to evaluate the new model. Results showed that the new method had a precision of 95.8%, a recall of 93.1%, and an overall accuracy (OA) of 94.7%. Compared with the traditional Mask R-CNN model, they have been significantly improved by 13.9%, 13.4%, and 9.9%, respectively. It was proved that the new method was effective in the landslides automatic extraction.

scholarly journals The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River

2021 ◽  
Author(s):  
Leijin Long ◽  
Feng He ◽  
Hongjiang Liu
Keyword(s):  
Remote Sensing ◽  
Southwest China ◽  
Influence Factors ◽  
Classification Error ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Jinsha River ◽  
Detection Model ◽  
High Level

AbstractIn order to monitor the high-level landslides frequently occurring in Jinsha River area of Southwest China, and protect the lives and property safety of people in mountainous areas, the data of satellite remote sensing images are combined with various factors inducing landslides and transformed into landslide influence factors, which provides data basis for the establishment of landslide detection model. Then, based on the deep belief networks (DBN) and convolutional neural network (CNN) algorithm, two landslide detection models DBN and convolutional neural-deep belief network (CDN) are established to monitor the high-level landslide in Jinsha River. The influence of the model parameters on the landslide detection results is analyzed, and the accuracy of DBN and CDN models in dealing with actual landslide problems is compared. The results show that when the number of neurons in the DBN is 100, the overall error is the minimum, and when the number of learning layers is 3, the classification error is the minimum. The detection accuracy of DBN and CDN is 97.56% and 97.63%, respectively, which indicates that both DBN and CDN models are feasible in dealing with landslides from remote sensing images. This exploration provides a reference for the study of high-level landslide disasters in Jinsha River.

scholarly journals A CloudSat–CALIPSO View of Cloud and Precipitation Properties across Cold Fronts over the Global Oceans

2015 ◽  
Vol 28 (17) ◽  
pp. 6743-6762 ◽  
Author(s):  
Catherine M. Naud ◽  
Derek J. Posselt ◽  
Susan C. van den Heever
Keyword(s):  
Large Scale ◽  
Cold Front ◽  
Conveyor Belt ◽  
Low Level ◽  
Scale Parameters ◽  
Convective Clouds ◽  
Cold Fronts ◽  
Surface Front ◽  
High Level ◽  
The Impact

Abstract The distribution of cloud and precipitation properties across oceanic extratropical cyclone cold fronts is examined using four years of combined CloudSat radar and CALIPSO lidar retrievals. The global annual mean cloud and precipitation distributions show that low-level clouds are ubiquitous in the postfrontal zone while higher-level cloud frequency and precipitation peak in the warm sector along the surface front. Increases in temperature and moisture within the cold front region are associated with larger high-level but lower mid-/low-level cloud frequencies and precipitation decreases in the cold sector. This behavior seems to be related to a shift from stratiform to convective clouds and precipitation. Stronger ascent in the warm conveyor belt tends to enhance cloudiness and precipitation across the cold front. A strong temperature contrast between the warm and cold sectors also encourages greater post-cold-frontal cloud occurrence. While the seasonal contrasts in environmental temperature, moisture, and ascent strength are enough to explain most of the variations in cloud and precipitation across cold fronts in both hemispheres, they do not fully explain the differences between Northern and Southern Hemisphere cold fronts. These differences are better explained when the impact of the contrast in temperature across the cold front is also considered. In addition, these large-scale parameters do not explain the relatively large frequency in springtime postfrontal precipitation.

A Study on the Control of Residual Stress of Copper Thick-Film Using 2D Nanomaterial

2021 ◽  
Vol 21 (12) ◽  
pp. 5960-5964
Author(s):  
Kwon Jai Lee ◽  
Jee Young Oh ◽  
Kyong Nam Kim
Keyword(s):  
Residual Stress ◽  
Heat Dissipation ◽  
High Efficiency ◽  
Rapid Development ◽  
Electronic Devices ◽  
Copper Film ◽  
Electronics Industry ◽  
Electronic Components ◽  
Graphene Layers ◽  
Electronic Parts

With the rapid development of the electronics industry, high-density electronic devices and component mounting have gained popularity. Because of the heat generated from these devices, efficiency of the electronic parts is significantly lowered and life of various electronic devices is considerably shortened. Therefore, it is essential to efficiently dissipate the heat generated from the device to extend product life and ensure high efficiency of electronic components. This study evaluated how residual stress is impacted by the thickness of the deposited copper film, which is widely used as a heat dissipation material, and the number of graphene layers. The results confirmed that the residual stress decreased with increasing thickness. Moreover, the residual stress changed based on the transfer area of graphene, which had an elastic modulus eight times that of copper, indicating that the residual stress of the deposited copper film can be controlled.

Hardware Context Switch-based Cryptographic Accelerator for Handling Multiple Streams

2021 ◽  
Vol 14 (3) ◽  
pp. 1-25
Author(s):  
Arif Sasongko ◽  
I. M. Narendra Kumara ◽  
Arief Wicaksana ◽  
Frédéric Rousseau ◽  
Olivier Muller
Keyword(s):  
Computation Time ◽  
Multiple Streams ◽  
Original Algorithm ◽  
Context Switching ◽  
Mode Of Operation ◽  
Synthesis Tool ◽  
Block Level ◽  
Key Block ◽  
Symmetric Key ◽  
High Level

The confidentiality and integrity of a stream has become one of the biggest issues in telecommunication. The best available algorithm handling the confidentiality of a data stream is the symmetric key block cipher combined with a chaining mode of operation such as cipher block chaining (CBC) or counter mode (CTR). This scheme is difficult to accelerate using hardware when multiple streams coexist. This is caused by the computation time requirement and mainly by management of the streams. In most accelerators, computation is treated at the block-level rather than as a stream, making the management of multiple streams complex. This article presents a solution combining CBC and CTR modes of operation with a hardware context switching. The hardware context switching allows the accelerator to treat the data as a stream. Each stream can have different parameters: key, initialization value, state of counter. Stream switching was managed by the hardware context switching mechanism. A high-level synthesis tool was used to generate the context switching circuit. The scheme was tested on three cryptographic algorithms: AES, DES, and BC3. The hardware context switching allowed the software to manage multiple streams easily, efficiently, and rapidly. The software was freed of the task of managing the stream state. Compared to the original algorithm, about 18%–38% additional logic elements were required to implement the CBC or CTR mode and the additional circuits to support context switching. Using this method, the performance overhead when treating multiple streams was low, and the performance was comparable to that of existing hardware accelerators not supporting multiple streams.

scholarly journals A Machine Learning Method for Vision-Based Unmanned Aerial Vehicle Systems to Understand Unknown Environments

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3245
Author(s):  
Tianyao Zhang ◽  
Xiaoguang Hu ◽  
Jin Xiao ◽  
Guofeng Zhang
Keyword(s):  
Machine Learning ◽  
Visual Servoing ◽  
Detection System ◽  
Detection Accuracy ◽  
Unknown Environment ◽  
Unknown Environments ◽  
Localization And Mapping ◽  
Computational Speed ◽  
Aerial Vehicle ◽  
High Level

What makes unmanned aerial vehicles (UAVs) intelligent is their capability of sensing and understanding new unknown environments. Some studies utilize computer vision algorithms like Visual Simultaneous Localization and Mapping (VSLAM) and Visual Odometry (VO) to sense the environment for pose estimation, obstacles avoidance and visual servoing. However, understanding the new environment (i.e., make the UAV recognize generic objects) is still an essential scientific problem that lacks a solution. Therefore, this paper takes a step to understand the items in an unknown environment. The aim of this research is to enable the UAV with basic understanding capability for a high-level UAV flock application in the future. Specially, firstly, the proposed understanding method combines machine learning and traditional algorithm to understand the unknown environment through RGB images; secondly, the You Only Look Once (YOLO) object detection system is integrated (based on TensorFlow) in a smartphone to perceive the position and category of 80 classes of objects in the images; thirdly, the method makes the UAV more intelligent and liberates the operator from labor; fourthly, detection accuracy and latency in working condition are quantitatively evaluated, and properties of generality (can be used in various platforms), transportability (easily deployed from one platform to another) and scalability (easily updated and maintained) for UAV flocks are qualitatively discussed. The experiments suggest that the method has enough accuracy to recognize various objects with high computational speed, and excellent properties of generality, transportability and scalability.

scholarly journals Object Detection Based on Region Decomposition and Assembly

2019 ◽  
Vol 33 ◽  
pp. 8094-8101 ◽  
Author(s):  
Seung-Hwan Bae
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Performance Improvement ◽  
Semantic Relations ◽  
Detection Accuracy ◽  
Semantic Features ◽  
Multi Scale ◽  
Object Proposals ◽  
Object Region ◽  
High Level

Region-based object detection infers object regions for one or more categories in an image. Due to the recent advances in deep learning and region proposal methods, object detectors based on convolutional neural networks (CNNs) have been flourishing and provided the promising detection results. However, the detection accuracy is degraded often because of the low discriminability of object CNN features caused by occlusions and inaccurate region proposals. In this paper, we therefore propose a region decomposition and assembly detector (R-DAD) for more accurate object detection.In the proposed R-DAD, we first decompose an object region into multiple small regions. To capture an entire appearance and part details of the object jointly, we extract CNN features within the whole object region and decomposed regions. We then learn the semantic relations between the object and its parts by combining the multi-region features stage by stage with region assembly blocks, and use the combined and high-level semantic features for the object classification and localization. In addition, for more accurate region proposals, we propose a multi-scale proposal layer that can generate object proposals of various scales. We integrate the R-DAD into several feature extractors, and prove the distinct performance improvement on PASCAL07/12 and MSCOCO18 compared to the recent convolutional detectors.

ELECTRONIC COMPONENT BASE MODERN MANAGEMENT ROAD TRANSPORT SYSTEM

2015 ◽  
Vol 2 (2) ◽  
pp. 803-806
Author(s):  
Новикова ◽  
Tatyana Novikova
Keyword(s):  
Transport System ◽  
Road Transport ◽  
Electronic Component ◽  
Electronic Systems ◽  
Electronic Components ◽  
Modern Management ◽  
Controls Systems

The article discusses trends and scope of electronic components in the vehicle controls systems that address some classification of the vehicle electronics, designated the requirements for electronic component base, used in the car electronic systems

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