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Novel green-fruit detection algorithm based on D2D framework

International Journal of Agricultural and Biological engineering 2022年第1期15卷 251-259,F0003页

作者： Jinmeng Wei Yanhui Ding Jie Liu Muhammad Zakir Ullah Xiang Yin Weikuan Jia School of Information Science and Engineering Shandong Normal UniversityJinan 250358China School of Agricultural Engineering and Food Science Shandong University of TechnologyZibo 255000ShandongChina Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry Zhenjiang 212013JiangsuChina

In the complex orchard environment,the efficient and accurate detection of object fruit is the basic requirement to realize the orchard yield measurement and automatic *** it is hard to differentiate between the object fruits and the background because of the similar color,and it is challenging due to the ambient light and camera angle by which the photos have been *** problems make it hard to detect green fruits in orchard *** this study,a two-stage dense to detection framework(D2D)was proposed to detect green fruits in orchard *** proposed model was based on multi-scale feature extraction of target fruit by using feature pyramid networks MobileNetV2+FPN structure and generated region proposal of target fruit by using Region Proposal Network(RPN)*** the regression branch,the offset of each local feature was calculated,and the positive and negative samples of the region proposals were predicted by a binary mask prediction to reduce the interference of the background to the prediction *** the classification branch,features were extracted from each sub-region of the region proposal,and features with distinguishing information were obtained through adaptive weighted pooling to achieve accurate *** new proposed model adopted an anchor-free frame design,which improves the generalization ability,makes the model more robust,and reduces the storage *** experimental results of persimmon and green apple datasets show that the new model has the best detection performance,which can provide theoretical reference for other green object detection.

关键词： green-fruit detection D2D framework automatic harvesting MobileNetV2+FPN binary mask prediction anchor-free

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Design of an Intelligent Voice-Controlled Home system Based on Raspberry Pi 25

Design of an Intelligent Voice-Controlled Home System Based ...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wu, Kaiyi Li, Jiaying Yu, Jiabin Hua, Yuhao Xiao, Binggang Wang, Wu College of Information Engineering China Jiliang University Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province Hangzhou China School of Computer Science and Technology Xidian University Xi'an China School of Psychological and Cognitive Sciences Peking University Multisensory Integration Laboratory Beijing China

ISBN: (纸本)9798350330014

A complete intelligent voice-controlled home system should include voice capture, voice recognition, authentication, voice result processing, and other processes. Based on the intelligent voice interaction model of Raspberry Pi 3b+ and Alibaba Cloud platform, this paper realized a high recognition accurate, stable, and efficient intelligent voice-controlled home system. We used the Raspberry Pi to control the hardware interface, collected voice data through an external microphone, and performed command control with external sensors and controllable devices in the hardware part;The software was based on Alibaba Cloud intelligent voice interaction platform to realize voice data recognition. The system constructed can achieve the expected function after repeatedly testing the controlled device. The test results show that the recognition accuracy and recognition efficiency of the system's voice control instructions are both high in a relatively quiet environment. © 2023 IEEE.

关键词： Speech recognition

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GAN-based Symmetric Embedding Costs Adjustment for Enhancing Image Steganographic Security 24

GAN-based Symmetric Embedding Costs Adjustment for Enhancing...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Ye, Miaoxin Zhou, Saixing Luo, Weiqi Tan, Shunquan Huang, Jiwu Guangdong Provincial Key Laboratory of Information Security Technology Sun Yat-Sen University Guangzhou China Shenzhen Key Laboratory of Media Security College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen MSU-BIT University Shenzhen China

ISBN: (纸本)9798400706868

Designing embedding costs is pivotal in modern image steganography. Many studies have shown adjusting symmetric embedding costs to asymmetric ones can enhance steganographic security. However, most existing methods heavily depend on manually defined parameters or rules, limiting security performance improvements. To overcome this limitation, we introduce an advanced GAN-based framework that transitions symmetric costs to asymmetric ones without the need for the manual intervention seen in existing approaches, such as the detailed specification of cost modulation directions and magnitudes. In our framework, we firstly achieve symmetric costs for a cover image, which is randomly split into two sub-images, with part of the secret information embedded into one. Subsequently, we design a GAN model to adjust the embedding costs of the second sub-image to asymmetric, facilitating the secure embedding of the remaining secret information. To support our phased embedding approach, our GAN's discriminator incorporates two steganalyers with different tasks: distinguishing the generator's final output, i.e., the stego image, from both the input cover image and the partially embedded stego image, providing diverse guidance to the generator. In addition, we introduce a simple yet effective update strategy to ensure a stable training process. Comprehensive experiments demonstrate that our method significantly enhances security over existing symmetric steganography techniques, achieving state-of-the-art levels compared to other methods focused on embedding costs adjustments. Additionally, detailed ablation studies validate our approach's effectiveness. © 2024 ACM.

关键词： Steganography

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STG-Siam:Siamese object tracking based on Sparse Self Attention and Graph information interaction 3

STG-Siam:Siamese object tracking based on Sparse Self Attent...

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3rd International Computing Imaging Conference, CITA 2023

作者： Yining, Yang Jinsong, Leng Hua, Cai Wancheng, Liu Yuhai, Li National Key Laboratory of Electromagnetic Space Security Tianjin300308 China School of electronic information engineering Changchun University of Science and Technology Changchun130022 China Changchun China Optical Science and Technology Museum Changchun130117 China

ISBN: (纸本)9781510671409

Aiming at the problems of insufficient foreground and background discrimination ability of the tracking algorithm in complex scenes, and limited ability to cope with the situation of partial occlusion of the target, we propose a target tracking algorithm based on sparse self-attention interaction with graph information (STG-Siam). To deal with background interference during tracking, a sparse self-attention mechanism is designed to make the model focus more on foreground information and suppress attention to background information. To better handle partial occlusions during tracking, template features and search region features are mapped to graph structures, and local information interaction between graph nodes is used to generate higher quality feature response maps. Experiments on the publicly available datasets VOT2018 and UAV123 demonstrate the effectiveness of the proposed STG-Siam, which can achieve accurate and robust tracking in complex scenarios. © 2023 SPIE.

关键词： Target tracking

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Environmental sound classification based on pitch shifting 3

Environmental sound classification based on pitch shifting

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3rd International Seminar on Computer science and engineering technology, SCSET 2022

作者： Zhao, Wen Yin, Bo Ocean University of China School of Information Science and Engineering Qingdao China Pilot National Laboratory for Marine Science and Technology Qingdao China

ISBN: (纸本)9781665478762

Environmental sound classification (ESC) is a challenging problem. Environmental sound classification is different from traditional music classification and speech classification because of its non-stationary characteristics. The use of convolutional neural network (CNN) to classify environmental sound is faced with the problems of unequal audio data sample length and single data, which leads to the generation of overfitting and low classification accuracy. In order to solve these problems, we proposed a waveform stretching method to solve the problem of unequal audio length, and adopted the audio data enhancement method with pitch shifting to prevent the over-fitting phenomenon caused by the single data. Log-Mel features are extracted and classified using an improved Lenet network. Our experimental results show that the proposed method achieves 95.65% accuracy on Urbansound8K. © 2022 IEEE.

关键词： Convolutional neural networks

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The Dynamics Simulation and Experimental Research of the No-back Device of the Horizontal Stabilizer Trim Actuator 18th

The Dynamics Simulation and Experimental Research of the ...

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18th Chinese Intelligent systems Conference, CISC 2022

作者： Xia, Tianxiang Ke, Bin Nanjing Engineering Institute of Aircraft System AVIC Nanjing210016 China Aviation Science and Technology Key Laboratory of Aviation Mechanical and Electrical System Nanjing210016 China

ISBN: (纸本)9789811962028

The horizontal stabilizer trim actuator could control the pitching angle by driving the horizontal stabilizer of the aircraft. The no-back device is one of the key components of the horizontal stabilizer trim actuator. It enables the horizontal stabilizer trim actuator to bear the compression or tension load from the horizontal stabilizer when it is not in motion, and ensures that the stabilizer actuator will not be driven reversely due to the aerodynamic load of the horizontal stabilizer. At the same time, in the process of trimming, the no-back device can release to ensure that it does not hinder the trimming function. In this paper, the working principle of the no-back device was firstly analyzed. Then Amesim and LMS Virtual Lab Motion were used to establish the co-simulation multi-body dynamic model to calculate the dynamic load of the structure and ANSYS Mechanical was used to establish the transient dynamic strength analysis model. The dynamic strength test was conducted. The experimental results were consistent with the tend of the simulation results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Actuators

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iGrasp: An Interactive 2D-3D Framework for 6-DoF Grasp Detection 27th

iGrasp: An Interactive 2D-3D Framework for 6-DoF Grasp Detec...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Jiang, Jian-Jian Wu, Xiao-Ming Chen, Zibo Wei, Yi-Lin Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783031781124

For 6-DoF grasp detection, we aim at introducing a new interactive 2D-3D framework which filters out irrelevant information and makes both modalities collaborate effectively to generate robust grasps and accelerate inference speed greatly. This cannot be accomplished by existing works on learning to grasp that merely utilize 3D point clouds or leverage both 2D textures and 3D point clouds. Our framework is called iGrasp, a novel three-step design between 2D textures and 3D point clouds, where the interaction modelling enhances both modalities. Concretely, we propose the 2D-to-3D interaction to leverage objectness masks generated from 2D textures to filter out target-irrelevant information in 3D point clouds. Then, we introduce the 3D-to-2D interaction to leverage structural priors from 3D point cloud features with cross-attention and cylinder grouping to refine 2D texture features. Finally, we combine the refined 2D texture features and 3D point cloud features for generating high-quality 6-DoF grasp poses. Our experiments on the large-scale real-world dataset, namely GraspNet-1Billion, demonstrate that iGrasp surpasses state-of-the-art methods by 4.66/3.53 mAP on RealSense/Kinect and reduces the inference time by 28%. Real-world experiments further verify the effectiveness of iGrasp. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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A Research of Visual-Inertial Simultaneous Localization and Mapping 3

A Research of Visual-Inertial Simultaneous Localization and ...

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3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Li, Jingbo Hamdulla, Askar Xinjiang University School of Information Science and Engineering Urumqi830017 China Xinjiang Key Laboratory of Multilingual Information Technology Urumqi830017 China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Simultaneous localization and mapping is a popular research topic in robotics, which aims to model the environment during the motion of a subject equipped with specific sensors, without a priori information about the environment. The ability to functionally complement two external sensors, the camera and the inertial measurement unit (IMU), gives it an advantage in terms of robustness. Therefore visual-inertial system (VINS) has a wide range of applications in the field of localization and mapping, including mobile robots, Automated driving systems (ADSs) 0 and aircraft. This study provides an introduction to the implementation of VINS for front-end odometry, back-end optimization, Loop closure detection and map building. Key problems and solutions in VINS, such as pose estimation, back-end optimization, and loop closure detection are highlighted. The characteristics of representative VINS methods are summarized to highlight the advantages of different method implementations. The whole process of VI-SLAM implementation is systematically discussed and combined with classical implementation methods, which helps to master the system implementation process and understand the differences of different working principles. This paper also presents the future trends and research directions of VINS, which can be used as a brief guide for novices and experienced researchers in the field of SLAM. © 2022 IEEE.

关键词： Cameras

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Towards Completeness: A Generalizable Action Proposal Generator for Zero-Shot Temporal Action Localization 27th

Towards Completeness: A Generalizable Action Proposal Genera...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Du, Jia-Run Lin, Kun-Yu Meng, Jingke Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783031784439

To address the zero-shot temporal action localization (ZSTAL) task, existing works develop models that are generalizable to detect and classify actions from unseen categories. They typically develop a category-agnostic action detector and combine it with the Contrastive Language-Image Pre-training (CLIP) model to solve ZSTAL. However, these methods suffer from incomplete action proposals generated for unseen categories, since they follow a frame-level prediction paradigm and require hand-crafted post-processing to generate action proposals. To address this problem, in this work, we propose a novel model named Generalizable Action Proposal generator (GAP), which can interface seamlessly with CLIP and generate action proposals in a holistic way. Our GAP is built in a query-based architecture and trained with a proposal-level objective, enabling it to estimate proposal completeness and eliminate the hand-crafted post-processing. Based on this architecture, we propose an Action-aware Discrimination loss to enhance the category-agnostic dynamic information of actions. Besides, we introduce a Static-Dynamic Rectifying module that incorporates the generalizable static information from CLIP to refine the predicted proposals, which improves proposal completeness in a generalizable manner. Our experiments show that our GAP achieves state-of-the-art performance on two challenging ZSTAL benchmarks, i.e., Thumos14 and ActivityNet1.3. Specifically, our model obtains significant performance improvement over previous works on the two benchmarks, i.e., +3.2% and +3.4% average mAP, respectively. The code is available at https://***/Run542968/GAP. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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A Secure Parameter Self-Tuning Control Scheme for Vehicle Platoons Under DoS Attacks

A Secure Parameter Self-Tuning Control Scheme for Vehicle Pl...

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IEEE International Conference on Cyber technology in Automation, Control, and Intelligent systems

作者： Qiaoni Han Guowen Cheng Zhenning Tang Chang Liu Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai China

In order to mitigate the adverse impact of denial of service (DoS) attacks, this work designs a parameter self-tuning controller based on fuzzy rules for vehicle platoons with vehicle-to-vehicle (V2V) communications. Aiming at achieving the cooperative adaptive cruise control (CACC) of the vehicles with a required inter-vehicle distance and the same velocity, while considering the possible DoS attacks, we propose a parameter self-tuning longitudinal controller combining linear control and fuzzy control to enhance the stability and resilience of the platoon systems. Firstly, by analyzing the dynamic characteristics of vehicles and the mechanism of DoS attacks, we establish the dynamic model of vehicle platoons and model the impact of random DoS attacks as a constant data transmission delay, whose probability distribution can be defined using the Bernoulli random variable. Then, a linear controller based on fuzzy control is designed for longitudinal control to guarantee that the platoon keeps a reference velocity and the specified inter-vehicle distance under DoS attacks. Finally, thorough simulations are performed and the detailed results show the performance of the secure longitudinal control scheme for vehicle platoons in terms of stability and security.

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