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Research on machine vision online monitoring system for egg production and quality in cage environment

POULTRY SCIENCE 2025年第1期104卷 104552页

作者： Wu, Zhenlong Zhang, Hengyuan Fang, Cheng South China Agr Univ Coll Engn Guangzhou 510642 Peoples R China State Key Lab Livestock & Poultry Breeding Guangzhou Peoples R China

In the domain of egg production, the application of automation technologies is essential for boosting productivity and quality. This study introduces an online monitoring system designed for egg quality assessment within caged environments, incorporating a robotic patrol system for egg localization and a fixed video stream for quality analysis. The project involved upgrading traditional henhouses with enhanced wireless connectivity and developing data transmission techniques for video streams and image data. The core of the system, an enhanced You Only Look Once Version 8-small (YOLOv8s) model, was augmented by substituting the Residual Network-18 backbone and integrating the Shuffle Attention mechanism, significantly improving egg detection precision. This refined model was implemented on Jetson AGX Orin industrial computer to facilitate real-world applications. To diverse operational needs, two distinct post-processing algorithms were developed: one for counting eggs and detecting abnormalities during robotic patrols, and another for assessing egg quality through fixed video streams, which measured crucial parameters such as egg dimensions and shape indexes. Experimental results revealed that the henhouse average network latencies of 35 ms, with signal strengths between -30 and -71 dBm, ensuring data transmission to the poultry management system. The enhanced YOLOv8s model, deployed on the Jetson AGX Orin, demonstrated well improvements: a Precision of 94.0% (+2.4 %), Recall rate of 92.8% (+4.6 %), Average Precision50:95 of 91.5 % (+3 %) and F1 score of 93.4 % (+3.9 %), with a minor decrease in detection speed to 91.7 Frame Per Second (-18.2). Field experiment in 60 chicken cages during robotic patrols achieved an egg recognition rate of 98.9 %, validating the system's effectiveness. In fixed settings, an 83-minute experiment managed to analyze egg numbers and abnormalities, attaining a 100 % recognition rate with all scoring data promptly relayed back to the mana

关键词： Egg Layered cage farming Object detection Poultry Precision livestock farming

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Quantum machine Learning for Computer vision: A Survey

Quantum Machine Learning for Computer Vision: A Survey

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International Conference on machine Learning and applications (ICMLA)

作者： Md Majedul Islam Jing Selena He Department of Computer Science Kennesaw State University Marietta USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

This research delves into quantum machine learning (QML) in the context of computer vision analysis by exploring the progress made in quantum computing and its impact on machine learning applications such as managing datasets and improving large-scale data processing efficiency through QML techniques specialised for tasks like image segmentation and classification in computer vision projects, along with findings, from trials conducted using the EMNIST benchmark *** tests reached an accuracy level above 90% successfully categorising tasks, with precision. This study explores the uses of quantum machine learning (QML) in areas like identification medical scans and distant monitoring. It also delves into the existing constraints and hurdles linked to quantum computer technologies.

关键词： Surveys Computer vision Adaptation models Quantum computing Accuracy Reviews Computational modeling machine learning Remote sensing Biomedical imaging

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Inverse design paradigm for fast and accurate prediction of a functional metasurface via deep convolutional neural networks

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OPTICAL MATERIALS EXPRESS 2022年第10期12卷 4104-4116页

作者： Du, Xudong Zhou, Chengan Bai, Hongbai Liu, Xingxing Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350116 Peoples R China

Data-driven deep learning frameworks have significantly advanced the development of modern machine learning, and after achieving great success in the field of image, speech, and video recognition and processing, they have also begun to permeate other disciplines such as physics, chemistry, and the discovery of new drugs and new materials. Our work proposes a deep learning-based model consisting of two parts: a forward simulation network that contains a transposed convolutional network, up and down sampling blocks and dense layers can rapidly predict optical responses from metasurface structures, and an inverse design network that contains convolutional neural networks and dense layers can automatically construct metasurface based on the input optical responses. Our model assists in discovering the complex and non -intuitive relationship between the moth-eye metasurface and optical responses, and designs a metasurface with excellent optical properties (ultra-broadband anti-reflection or nonlinear function of reflectivity), while avoiding traditional time-consuming case-by-case numerical simulations in the metasurface design. This work provides a fast, practical, and robust method to study complex light-matter interactions and to accelerate the demand-based design of nanophotonic devices, opening a new avenue for the development of real nanophotonic applications.

关键词： Genetic algorithms Light matter interactions machine vision Neural networks Numerical simulation Optical properties

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A Novel Resource-Constrained Insect Monitoring System based on machine vision with Edge AI 5

A Novel Resource-Constrained Insect Monitoring System based ...

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5th IEEE International Conference on image processing applications and Systems (IPAS)

作者： Kargar, Amin Wilk, Mariusz P. Zorbas, Dimitrios Gaffney, Michael T. O'Flynn, Brendan Univ Coll Cork Tyndall Natl Inst Cork Ireland Nazarbayev Univ Dept Comp Sci Nur Sultan Kazakhstan Teagasc Ashtown Food Res Ctr Hort Dev Dept Dublin Ireland

ISBN: (纸本)9781665462198

Effective insect pest monitoring is a vital component of Integrated Pest Management (IPM) strategies. It helps to support crop productivity while minimising the need for plant protection products. In recent years, many researchers have considered the integration of intelligence into such systems in the context of the Smart Agriculture research agenda. This paper describes the development of a smart pest monitoring system, developed in accordance with specific requirements associated with the agricultural sector. The proposed system is a low-cost smart insect trap, for use in orchards, that detects specific insect species that are detrimental to fruit quality. The system helps to identify the invasive insect, Brown Marmorated Stink Bug (BMSB) or Halyomorpha halys (HH) using a Microcontroller Unit-based edge device comprising of an Internet of Things enabled, resource-constrained image acquisition and processing system. It is used to execute our proposed lightweight image analysis algorithm and Convolutional Neural Network (CNN) model for insect detection and classification, respectively. The prototype device is currently deployed in an orchard in Italy. The preliminary experimental results show over 70 percent of accuracy in BMSB classification on our custom-built dataset, demonstrating the proposed system feasibility and effectiveness in monitoring this invasive insect species.

关键词： machine vision image processing Deep Learning Edge AI Integrated Pest Monitoring Food Security

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VISTA: A Visual and Textual Attention Dataset for Interpreting Multimodal Models

VISTA: A Visual and Textual Attention Dataset for Interpreti...

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IEEE Winter applications and Computer vision Workshops (WACVW)

作者： Harshit Tolga Tasdizen School of Computing University of Utah Salt Lake City UT USA Scientific Computing and Imaging Institute University of Utah Salt Lake City UT USA

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

The recent developments in deep learning (DL) led to the integration of natural language processing (NLP) with computer vision, resulting in powerful integrated vision and Language Models. Despite their remarkable capabilities, these models are frequently regarded as black boxes within the machine learning research community. This raises a critical question: which parts of an image correspond to specific segments of text, and how can we decipher these associations? Understanding these connections is essential for enhancing model transparency, interpretability, and trustworthiness. To answer this question, we present an image-text aligned human visual attention dataset (VISTA) 1 1 The data is available at https://***/h-pal/Data-for-VISTA that maps specific associations between image regions and corresponding text segments. We then compare the internal heatmaps generated by VL models with this dataset, allowing us to analyze and better understand the model's decision-making process. This approach aims to enhance model transparency, interpretability, and trustworthiness by providing insights into how these models align visual and linguistic information. We conducted a comprehensive study on text-guided visual saliency detection in these VL models. This study aims to understand how different models prioritize and focus on specific visual elements in response to corresponding text segments, providing deeper insights into their internal mechanisms and improving our ability to interpret their outputs.

关键词： Measurement Visualization image segmentation Computer vision Analytical models Computational modeling machine vision Natural language processing Reliability Saliency detection

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CMOS image sensor for wide dynamic range feature extraction in machine vision

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ELECTRONICS LETTERS 2021年第5期57卷 206-208页

作者： Kim, Hyeon-June Kangwon Natl Univ Dept Elect Informat Commun Engn Gangwon South Korea

This letter presents a wide dynamic range (WDR) feature extraction (FE) readout scheme for machine vision applications using CMOS image sensors (CISs). The proposed scheme with the proposed pixel structure has two operating modes, the normal and WDR modes. In the normal operating mode, the proposed CIS captures a normal image with high sensitivity. In addition, as a unique function, a bi-level image is obtained for real-time FE even if a pixel is saturated in strong illumination conditions. Thus, compared to typical CISs for machine vison, the proposed CIS can reveal object features that are blocked by light in real time. In the WDR operating mode, the proposed CIS produces a WDR image with its corresponding bi-level image. A prototype CIS was fabricated using a standard 0.35-mu m 2P4M CMOS process with a 320 x 240 format (QVGA) with 10-mu m pitch pixels. At 60 fps, the measured power consumption was 5.98 mW at 3.3 V for pixel readout and 2.8 V for readout circuitry. The dynamic range of 73.1 dB was achieved in the WDR operating mode.

关键词： image recognition image sensors Computer vision and image processing techniques

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Yolov3-Pruning(transfer): real-time object detection algorithm based on transfer learning

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JOURNAL OF REAL-TIME image processing 2022年第4期19卷 839-852页

作者： Li, Xiaoning Wang, Zhengzhong Geng, Shichao Wang, Lin Zhang, Huaxiang Liu, Li Li, Donghua Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China Shandong Normal Univ Sch Journalism & Commun Jinan 250014 Shandong Peoples R China Shandong Normal Univ Inst Data Sci & Technol Jinan 250014 Shandong Peoples R China

In recent years, object detection algorithms have achieved great success in the field of machine vision. To pursue the detection accuracy of the model, the scale of the network is constantly increasing, which leads to the continuous increase in computational cost and a large requirement for memory. The larger network scale allows their execution to take a longer time, facing the balance between the detection accuracy and the speed of execution. Therefore, the developed algorithm is not suitable for real-time applications. To improve the detection performance of small targets, we propose a new method, the real-time object detection algorithm based on transfer learning. Based on the baseline Yolov3 model, pruning is done to reduce the scale of the model, and then migration learning is used to ensure the detection accuracy of the model. The object detection method using transfer learning achieves a good balance between detection accuracy and inference speed and is more conducive to the real-time processing of images. Through the evaluation of the dataset voc2007 + 2012, the experimental results show that the parameters of the Yolov3-Pruning(transfer): model are reduced by 3X compared with the baseline Yolov3 model, and the detection accuracy is improved, realizes real-time processing, and improves the detection accuracy.

关键词： Object detection Transfer learning Pruning Detection accuracy Inference speed Real-time processing

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Remote Sensing image Captioning (RSIC): A Technical Review 1st

Remote Sensing Image Captioning (RSIC): A Technical Review

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1st International Conference on Data Engineering and machine Intelligence, ICDEMI 2023

作者： Dhinesh, A. Sumathy, P. Department of Computer Science Bharathidasan University Tamilnadu Tiruchirappalli620023 India

ISBN: (纸本)9789819776153

Remote Sensing image Captioning (RSIC) is crucial for many researchers since it has many applications in environmental monitoring, disaster management, urban planning, image retrieval, performance of building planes, military intelligence, and autonomous vehicles. The effective procedure to generate the captions from remote sensing images complements the above-mentioned application domains. Various baseline data sets have been created by the researchers to enhance the quality of captioning by processing the diverse features of the geospatial information. In this paper, we have technically reviewed important literature that follow different algorithms for generating the captions. For example, we have presented the technical review on vision-Language Aligning Paradigm (VLCA) under the bi-lingual caption generation model, Joint-Training Two-Stage (JTTS) technique under multimodel fusion category, Multilevel and Contextual Attention Network (MLCA-Net) under context-aware captioning, LEVIR-CC belongs to transfer learning model, BERT and GPT-3 models belong to transfer-based model, Multiscale Attention (MSA) and Multifeat Attention (MFA) of Multiscale captioning model and Summarization Driven (SD)-RSIC of fine-grained captioning model. We have also presented the performance of each of these methods on various benchmark datasets. For evaluation, different well-known performance metrics are considered. The result is critically evaluated and commented on. In the future, a more rigorous review of these methods along with other relevant methods will be presented along with implementation data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Urban planning

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Real-Time Object Detection and Tracking Design Using Deep Learning with Spatial–Temporal Mechanism for Video Surveillance applications 10th

Real-Time Object Detection and Tracking Design Using Deep Le...

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10th International Conference on Innovations in Computer Science and Engineering, ICICSE 2022

作者： Kusuma, T. Ashwini, K. Global Academy of Technology Bangalore India

ISBN: (纸本)9789811974540

We propose a CNN-based framework for "real-time object detection and tracking using deep learning" in this paper, which includes a spatial–temporal mechanism. The impact of efficient data on performance benchmarks in terms of accuracy has changed. The data processing is handled by industry buzzwords: deep learning (DL) and computer vision (CV). The CNN-based framework uses the single object tracker value to match arrival models and find targets in the next frame. Simply applying single object tracking to multiple object tracking will encounter problems in computational efficiency and results due to occlusion. In this paper, we introduce a "spatial attention mechanism (STAM)" to manage occlusion bias and target interaction. Object tracking is a sensational technology in image processing with great future implications. Multiple object tracking (MOT) has seen an extensive boom in the last few years due to machine learning, deep learning, computer vision, and more. This paper aims to provide an object tracking software solution. Using YOLO’s "You Only Look Once" technology with the help of Tensor flow, the system is geared toward object detection, tracking, and counting. Proven, effective detection and tracking on various dataset. Algorithms that offer real-time, accurate, and precise identifications appropriate for real-time applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Advancements in low light image enhancement techniques and recent applications

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JOURNAL OF VISUAL COMMUNICATION AND image REPRESENTATION 2024年 103卷

作者： Anoop, P. P. Deivanathan, R. Vellore Inst Technol Sch Mech Engn Chennai 600127 India

Low-light image enhancement is an effective solution for improving image recognition by both humans and machines. Due to low illuminance, images captured in such conditions possess less color information compared to those taken in daylight, resulting in occluded images characterized by distortion, low contrast, low brightness, a narrow gray range, and noise. Low-light image enhancement techniques play a crucial role in enhancing the effectiveness of object detection. This paper reviews state-of-the-art low-light image enhancement techniques and their developments in recent years. Techniques such as gray transformation, histogram equalization, defogging, Retinex, image fusion, and wavelet transformation are examined, focusing on their working principles and assessing their ability to improve image quality. Further discussion addresses the contributions of deep learning and cognitive approaches, including attention mechanisms and adversarial methods, to image enhancement.

关键词： Computer vision Low-light image enhancement Deep learning image processing machine learning image quality assessment

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