Results 21 to 30 of about 12,664 (249)
Tạp chí Khoa học và Công nghệThis study evaluates the effectiveness of various detection-based object-tracking algorithms to optimize accuracy and efficiency in traffic flow monitoring. Due to its high accuracy in detecting objects, YOLOv8 was chosen as the vehicle detector for this
Hai T. Ton +6 more
doaj +1 more source
Cytometry Part A, EarlyView.ABSTRACT We present a new end‐to‐end neural network approach for real‐time biological cell detection and classification via label‐free quantitative imaging flow cytometry based on digital holography, offering a comprehensive representation of cellular structures without the need for chemical cell staining. In contrast to previous studies, our method is
Dana Yagoda‐Aharoni +3 more
wiley +1 more source
CDF-YOLOv8: City Recognition System Based on Improved YOLOv8
IEEE AccessTo address challenges in urban traffic management, especially detection under low exposure conditions and image quality degradation caused by weather factors, this paper proposes an urban detection algorithm based on the YOLOv8 model. Initially, The idea introduced the Chain-of-Thought Prompt Adaptive Enhancer (CPA-enhancer) to enhance image processing
P. Lu, Y. S. Jia, W. X. Zeng, P. Wei
openaire +2 more sources
CSD-YOLOv8s: Dense Sheep Small Target Detection Model Based on UAV Images
智慧农业ObjectiveThe monitoring of livestock grazing in natural pastures is a key aspect of the transformation and upgrading of large-scale breeding farms. In order to meet the demand for large-scale farms to achieve accurate real-time detection of a large ...
WENG Zhi, LIU Haixin, ZHENG Zhiqiang
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A “Hardware-Friendly” Foreign Object Identification Method for Belt Conveyors Based on Improved YOLOv8 [PDF]
, 2023 Bingxin Luo +3 more
openalex +1 more source
Using Convolutional Neural Networks for the Classification of Suboptimal Chest Radiographs
Journal of Medical Radiation Sciences, EarlyView.This study evaluated DenseNet121 and YOLOv8 neural networks in detecting suboptimal chest x‐rays for quality control. Through training, validation, and testing, both AI models effectively classified chest X‐ray quality, highlighting the potential to provide radiographers with feedback to enhance image quality.
Emily Huanke Liu +2 more
wiley +1 more source
BSMD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection
Applied SciencesIn the field of bookbinding, accurately and efficiently detecting signature sequences during the binding process is crucial for enhancing quality, improving production efficiency, and advancing industrial automation. Despite significant advancements in object detection technology, verifying the correctness of signature sequences remains challenging due
Long Guo +3 more
openaire +2 more sources
A Regional Farming Pig Counting System Based on Improved Instance Segmentation Algorithm
智慧农业ObjectiveCurrently, pig farming facilities mainly rely on manual counting for tracking slaughtered and stored pigs. This is not only time-consuming and labor-intensive, but also prone to counting errors due to pig movement and potential cheating.
ZHANG Yanqi +4 more
doaj +1 more source
An improved pistachio detection approach using YOLO-v8 Deep Learning Models [PDF]
BIO Web of ConferencesPistachios are an agricultural product widely used in the food industry. It is very important that pistachios are presented to the consumer in good quality on time.
Gökalp Çınarer +1 more
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Artificial Intelligence for Radiographic Image Quality: Radiographers at the Forefront
Journal of Medical Radiation Sciences, EarlyView.This editorial highlights the central role of radiographers in leading AI‐driven radiographic image‐quality assessment. It outlines how AI can enhance real‐time feedback, support consistency, and strengthen safe, patient‐centered imaging practice.
Kamarul Amin Abdullah
wiley +1 more source