Current Issue : July-September Volume : 2025 Issue Number : 3 Articles : 5 Articles
We show non-invasive 3D plant disease imaging using automated monocular vision-based structure from motion. We optimize the number of key points in an image pair by using a small angular step size and detection in the extra green channel. Furthermore, we upsample the images to increase the number of key points. With the same setup, we obtain functional fluorescence information that we map onto the 3D structural plant image, in this way obtaining a combined functional and 3D structural plant image using a single setup....
Modern poultry and egg production is facing challenges such as dead chickens and floor eggs in cage-free housing. Precision poultry management strategies are needed to address those challenges. In this study, convolutional neural network (CNN) models and an intelligent bionic quadruped robot were used to detect floor eggs and dead chickens in cage-free housing environments. A dataset comprising 1200 images was used to develop detection models, which were split into training, testing, and validation sets in a 3:1:1 ratio. Five different CNN models were developed based on YOLOv8 and the robot’s 360◦ panoramic depth perception camera. The final results indicated that YOLOv8m exhibited the highest performance, achieving a precision of 90.59%. The application of the optimal model facilitated the detection of floor eggs in dimly lit areas such as below the feeder area and in corner spaces, as well as the detection of dead chickens within the flock. This research underscores the utility of bionic robotics and convolutional neural networks for poultry management and precision livestock farming....
Manufacturing, technology, and society experienced a number of changes throughout the Industrial Revolution. This paper presents a comprehensive review of advancements in Machine Vision Systems (MVS) for smart manufacturing and industrial quality control inspections, drawing from a range of studies that highlight advancements in optical systems, image acquisition techniques, and the pivotal role of deep learning methodologies. With the advent of AI and deep learning, MVS have achieved unprecedented levels of accuracy, speed, and versatility. It addresses key components of visual inspection systems, including optical illumination, image acquisition, and image processing, examining their impact on detection accuracy, efficiency, and robustness. The review identifies prevalent methods and future trends, offering insights for researchers and practitioners in manufacturing, computer vision, and quality control. Ultimately, this review aims to present the recent trends in the field of machine vision and image processing for defect detection in industrial applications, along with highlighting the research gaps for future work. Manufacturing processes inherently transform raw materials into finished products through a series of intricate operations that significantly impact the production line's overall efficiency and output quality. Defects and inconsistencies can lead to substantial economic losses, reputational damage, and potential safety hazards. The integration of machine vision systems has significantly enhanced defect detection in industrial manufacturing by improving efficiency, quality, and reliability. MVS have wide applications across industries, namely, automotive industry, electronics manufacturing, food and beverage industry, pharmaceutical industry and textile industry. Despite the numerous benefits of MVS, several challenges exist related to technical constraints, data requirements, adaptability and generalisation, and computational resources. The review concluded that Machine Vision Systems (MVS) have emerged as a critical component of modern industrial quality control, offering unparalleled capabilities for real-time monitoring, defect detection, and process automation. The integration of Artificial Intelligence (AI) and deep learning has further enhanced the performance and versatility of MVS, enabling them to tackle complex inspection tasks with remarkable accuracy and efficiency....
A pivotal moment in the leap toward autonomous vehicles in recent years has revealed the need to enhance vehicle-to-everything (V2X) communication systems so as to improve road safety. A key challenge is to integrate real-time pedestrian detection to permit the use of timely alerts in situations where vulnerable road users, especially pedestrians, might pose a risk. Seeing that, in this article, a YOLO-based object detection model was used to identify pedestrians and extract key data such as bounding box coordinates and confidence levels. These data were encoded afterward into decentralized environmental notification messages (DENM) using ASN.1 schemas to ensure compliance with V2X standards, allowing for real-time communication between vehicles and infrastructure. This research identified that the integration of pedestrian detection with V2X communication brought about a reliable system wherein the roadside unit (RSU) broadcasts DENM alerts to vehicles. These vehicles, upon receiving the messages, initiate appropriate responses such as slowing down or lane changing, with the testing demonstrating reliable message transmission and high pedestrian detection accuracy in simulated–controlled environments. To conclude, this work demonstrates a scalable framework for improving road safety by combining machine vision with V2X communication....
A machine vision-based weld seam positioning system for thermos cups is proposed. The system’s operational flow and image algorithms are designed to achieve automatic calibration of the weld seam orientation. Experimental results show a qualification rate of 97.8%, demonstrating robust performance across various cup models. The average positioning error is 45 pixels, with a maximum error of 153 pixels, corresponding to angular deviations of 0.68◦ and 2.30◦, respectively. The average processing time of a single algorithm run is 128 ms, ensuring efficient operation in non-high-speed production scenarios. The results of this study have good application value and also provide some insights for the position calibration of other rotational objects....
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