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Lightweight vegetable detection algorithm based on improved YOLOv5 3

Lightweight vegetable detection algorithm based on improved ...

引用

3rd international conference on computer vision and Data Mining, ICCVDM 2022

作者： Guo, Song Zhang, QingYao School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9781510661363

With the rapid development of deep learning, vegetable detection based on computer vision in smart supermarkets can save a lot of manpower. To solve the problem that the reported target detection algorithm fails to balance detection accuracy and model size well, a lightweight vegetable detection method that is based on yolov5 model was proposed in this work. The method firstly improved the detection accuracy by adding a fusion attention mechanism (Convolutional Block Attention Module) in the backbone part, and then reduces the model parameters by replacing the normal convolution in the network with Ghost convolution to ensure the accuracy at the same time. The accuracy of localization was improved by using Alpha-IoU as the loss function of the bounding box regression. The experimental results show that the improved method achieves an average accuracy of 92.5%, which is 3.2% higher than yolov5, and the total model parameters are 6.1 MB, which is 1.0 MB lower than yolov5, meeting the requirement of balancing detection accuracy and model size. © COPYRIGHT SPIE.

关键词： Vegetables

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IoT-based Car Driver Assistance System using computer vision and Machine Learning 3

IoT-based Car Driver Assistance System using Computer Vision...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Shubham, Kumar Satyam, Kumar Kapisway, Pranav Memani, Raghav Anand, Aniket Sathiya Kumar, C. VIT Vellore632014 India

ISBN: (纸本)9798350372120

This paper presents an innovative approach for improving driver convenience and safety by combining Artificial Intelligence (AI) and Internet of Things (IoT) with computer vision techniques like OpenCV. By using intelligent automation, the suggested system, termed the IoT-based Car Driver Assistance System using computer vision and Machine Learning, seeks to both enhance and reduce potential driving-related risks. A camera and sensor suite are installed on the dashboard of the vehicle as part of the system design to enable real-time data collection and processing. By utilizing face recognition algorithms, the system begins by confirming the driver's identification prior to vehicle activation, which improves security. The system always keeps a watch on the driver's condition, looking for indications of drowsiness while operating and sending out alerts in a timely manner to help avoid collisions. The technology improves situational awareness by detecting and alerting the driver to approaching obstacles on the road, helping the driver avoid potentially dangerous situations. The technology also facilitates hands-free driving by utilizing hand gesture detection to provide seamless control over in-car audio operations such as track selection and volume adjustment. Call management is integrated with gesture recognition, enabling drivers to use hand gestures to accept or reject incoming calls. The system aims to improve the driving experience by utilizing facial recognition and mood detection to identify the driver's emotional state and provide tailored Spotify music choices. By recognising traffic signals and lane changes, the technology helps drivers maintain lane discipline, which improves road safety and encourages adherence to traffic laws. In order to lessen the difficulties of parking in crowded areas, the system also offers intelligent parking assistance. With a full feature set that gives drivers more control, awareness, and efficiency while driving, the proposed I

关键词： Internet of things

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Fault Detection in Bottle Caps and Label Alignment Using Convolutional Neural Network 3

Fault Detection in Bottle Caps and Label Alignment Using Con...

引用

3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Tatu, Sapana Indarchand Singh, Shashank Kumar Bansod, Saurabh Pal, Prashant National Institute of Electronics and Information Technology Department of EDT Aurangabad India National Institute of Electronics and Information Technology Aurangabad India

ISBN: (纸本)9781665494007

Quality control is one of the most crucial phases in the creation of any product. Product quality is the degree to which a product lives up to expectations. Due to flaws in bottle caps and label alignment, the quality of beverage products suffers. To overcome this problem, detection of fault via computer-based methods is a developing trend across many industries. In this paper by using Convolutional neural networks, image processing is used to detect fault in bottle caps which include loose caps, tilted caps, and missing caps and edge detection method is used to detect fault in label alignment which includes misplaced or tilted labels and inverted labels. The proposed model will improve precision and speed of fault detection. Due to the accuracy and time concession the adaptability of this machine learning method, the system can operate in a variety of lighting and backdrop conditions. © 2023 IEEE.

关键词： computer vision

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Automated Depression Diagnosis Based on Facial Expression and Deep Dual-Stream Convolutional Neural Networks 3

Automated Depression Diagnosis Based on Facial Expression an...

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3rd international conference on computer vision and Pattern Analysis, ICCPA 2023

作者： Chen, Yangyang Chen, Yingyu Li, Mi Department of Automation Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

ISBN: (纸本)9781510667563

Depression is a common mental illness characterized by symptoms such as low mood, pessimism, and insomnia. In this study, we developed a deep Dual-Stream CNN to automatically diagnose and classify depression in expression video sequences. The network has two branches that extract static features and dynamic features from static and dynamic expressions, respectively, which are then fused for depression classification. The experiments were performed on the AVEC2014 database, and the results showed that the Dual-Stream model significantly improved the classification performance of depression, achieving an accuracy of 69.08% in depression categorization. © 2023 SPIE.

关键词： Convolutional neural networks

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Design And Trial Production Of Flying Flowers Detection And Information Processing Device Basis On Line Scan Camera 3

Design And Trial Production Of Flying Flowers Detection And ...

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3rd international conference on Robotics, Automation and Intelligent control, ICRAIC 2023

作者： Wang, Yuefei Yu, Kai Si, Youyin Duan, Kaiwen Jiang, Ziyan Gu, Haibing Yang, Zhanping Shen, Lin Nantong Cellulose Fibers CO LTD Jiangsu China Jiangsu University School of Food and Biological Engineering Jiangsu China Jiangsu University School of Agricultural Engineering Jiangsu China

ISBN: (纸本)9798350360813

To address the inefficiency and error-prone nature of manual weighing methods used to detect the presence of short fibers, commonly known as 'flying flowers,' in the acetate fiber tow used to make cigarette filter rods, a line scan camera-based visual inspection system was developed. This system operates on an open flying flower integrated machine, where the tow is subjected to specified speed ratios, and roll pressures on the roll, resulting in the opening and stretching of the tow. However, due to factors such as fiber wear and breakage, a certain amount of short fibers are produced. The newly designed visual inspection system aims to overcome the limitations of manual weighing methods and provide improved efficiency and accuracy in detecting the flying flower. This paper mainly focuses on presenting the comprehensive design scheme of the system, as well as discussing the structural composition and working principles of each component. The main objectives achieved include the automation of microfiber scene capture after manual sampling on the production line, the implementation of encoding, storage, reading and control of shooting light sources, and the successful development of Feihua intelligent detection system. In addition, through these information processing and development efforts, significant progress has been made in key technologies related to Feihua intelligent detection. © 2023 IEEE.

关键词： computer vision

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Research on Heterogeneous Converged Network Handover Algorithm for Mountainous Microgrid 3

Research on Heterogeneous Converged Network Handover Algorit...

引用

3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Shi, Zhan Li, Xingnan Kang, Zhongmiao Guangdong Power Grid Co. Ltd. Power Dispatching Control Center Communication Management Department Guangdong China

ISBN: (纸本)9781665459112

The communication network for mountainous microgrid has the shortcomings of weak network infrastructure, low communication coverage, and single communication mode, which cannot satisfy the differentiated communication requirements of the mountainous microgrid services. This paper proposes a heterogeneous converged communication network architecture that integrates power line communication (PLC) and micro-power wireless communication, and proposes a heterogeneous converged network handover algorithm based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and the improved Analytic Hierarchy Process (AHP). The simulation results show that the proposed handover algorithm can analyze the differentiated requirements of different types of mountainous microgrid services, select the most suitable communication mode, and achieve effective network handover. © 2022 IEEE.

关键词： Analytic hierarchy process

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Deep learning algorithm for Facial Expression Classification 3

Deep learning algorithm for Facial Expression Classification

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3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： He, Bin HeBei Geo University Department of Software Engineering Shijiazhuang050030 China

ISBN: (纸本)9781665459112

The research on facial expression recognition is undoubtedly a hot topic in both academia and industry. In today's world, facial expression recognition technology is widely used in everyday life, such as fatigue driving detection. In this paper, this paper mainly analyses the performance impact of different depth networks on facial expression recognition. We use AlexNet, VGG and ResNet for comparative analysis. The experiments on the FER2013 dataset show that different depth networks have different performances for facial expression recognition, not deeper networks equal to better performance. © 2022 IEEE.

关键词： computer vision

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A Smart Irrigation System for Coconut Farming using IoT 3

A Smart Irrigation System for Coconut Farming using IoT

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Russia, S. Mohanapriya, B. Gowrishankar, V. Parimala Devi, M. Pradeep, K.K. Brindha, P. Velalar College of Engineering and Technology Department of Computer Science and Engineering India Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode India Erode Sengunthar Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Coconut farming is an essential agricultural practice that contributes significantly to the global economy by providing valuable products such as coconut water, oil, and meat. However, the management of coconut plantations faces numerous challenges, including labor shortages, pest control, and the demand for precise irrigation and fertilization. This paper proposes an integrated smart farming system that leverages advanced sensor technologies and automation to address these challenges. The system employs precision irrigation and fertigation methods, facilitated by a centralized motor controller and solenoid valves, to deliver water and nutrients efficiently to the base of each coconut tree. A pH sensor monitors soil acidity or alkalinity, ensuring optimal nutrient availability and promoting sustainable farming practices by reducing chemical inputs. Additional features include a tree motion sensor and a tilt sensor for monitoring tree stability under adverse weather conditions. Soil moisture sensors, temperature, humidity, and light intensity sensors provide real-time data to enhance decision-making. Farmers can remotely monitor and control the system via a Node-RED dashboard, receiving alerts through platforms like Telegram. By integrating these technologies, the proposed system aims to optimize resource utilization, enhance crop yield, and reduce the environmental impact of coconut farming. © 2025 IEEE.

关键词： Soil moisture

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Natural language understanding and interaction engine oriented to human-computer interaction based on neural network 3

Natural language understanding and interaction engine orient...

引用

3rd international conference on computer vision and Data Mining, ICCVDM 2022

作者： Ma, Yongbo Zhang, Li Wang, Xiaoyi Customer Service Center of SGCC Tianjin300309 China

ISBN: (纸本)9781510661363

In recent years, due to the continuous advancement of computer software and hardware and the advancement of artificial intelligence, the advancement of human-machine communication systems has made considerable progress. The natural language understanding module is the basic element of the human-computer communication system, and the result of its semantic understanding will have a significant impact on the development of later components, and directly affect the process and the improvement of the success rate of human-computer interaction. In this paper, the neural network-oriented human-computer interaction-oriented natural language understanding and interaction engine is researched. On the basis of literature data, the relevant knowledge of human-computer interaction is understood, and then the neural network-oriented human-computer interaction natural language understanding is designed on the interactive engine, and the neural network reasoning structure model it quotes is tested. The test results show that the RMNN model used in this paper has achieved an accuracy of 70.24%, and the significance test p-value is 0.001. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Engines

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Smart vision Software Application using Machine Learning 3

Smart Vision Software Application using Machine Learning

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3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Alagarsamy, Saravanan Deepak, Prudhivi Lavanya, M. Reddy, T. Gowtham Kedareswari, M. Senthil Kumar, A. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Anand Nagar Tamilnadu Krishnankoil India Aarupadai Veedu Institute of Technology Department of Mechanical and Engineering Tamilnadu India

ISBN: (纸本)9781665462167

The Smart vision Application's premise is that there are numerous rising new technologies that are excelling in their fields. The following are a few of the technologies or models that are now in use: estimation of human pose, steering angle capture, lane detection, and object detection. These are all the various approaches and superb models created with Open Pose and other tools. Since each of these systems has unique characteristics, it is vital to separately construct each one before comprehending how it works. Because there is no trial version available for consumers to use to learn how the model works, these models must be constructed using codes creating a web application that will enable students to learn about and experience how each model functions by using the camera on their device. For business professionals who can use their own models to run, deploy, and test, not simply for users. Every module on the list has some connection to autonomous navigation. These systems have been combined into a single Web application so that students may easily experiment with them and see how they work in real-time. As a result, this platform presents excellent opportunities for students and enthusiastic learners to interact with the live demo and understand how each model functions. It is believed that the Web application will serve as an excellent tool for students to experiment with and gain a feel for the operation of the aforementioned computer vision models. © 2023 IEEE.

关键词： Students

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