دانلود و نمایش مقالات مرتبط با Computer vision approaches::صفحه 1
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نتیجه جستجو - Computer vision approaches

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Computer vision based food grain classification: A comprehensive survey
طبقه بندی دانه های غذایی مبتنی بر بینایی رایانه ای: یک مرور جامع-2021
This manuscript presents a comprehensive survey on recent computer vision based food grain classification techniques. It includes state-of-the-art approaches intended for different grain varieties. The approaches pro- posed in the literature are analyzed according to the processing stages considered in the classification pipeline, making it easier to identify common techniques and comparisons. Additionally, the type of images considered by each approach (i.e., images from the: visible, infrared, multispectral, hyperspectral bands) together with the strategy used to generate ground truth data (i.e., real and synthetic images) are reviewed. Finally, conclusions highlighting future needs and challenges are presented.
Keywords: Computer vision approaches | Quality inspection | Food grain identification | Machine vision
مقاله انگلیسی
2 Computer vision approaches for detecting missing barricades
رویکردهای بینایی ماشین برای تشخیص موانع گمشده-2021
The installation of barricades effectively prevents falls from height (FFH) on construction sites. Common approaches for detecting missing barricades (e.g., manual inspection of the site or three-dimensional models) are not practical due to two inherent challenges: (1) these approaches are labor-intensive and time-consuming; and(2) FFH hazards are dynamic and changing as construction work progresses. To address these challenges, two computer vision-based detection approaches, including Masks Comparison Approach (MCA) and Missing Object Detection Approach (MODA), are developed in this study to automatically detect missing barricade. The performance of the proposed approaches and their benefits and implementation challenges were evaluated through a case study. The results demonstrate that MODA can achieve better performance and have several implementation advantages over MCA. The average precision and average recall for MODA were 57.9% and 73.6%, respectively. These two approaches can help site managers take action promptly to reduce the risks of FFH accidents.
Keywords: Falls from height | Safety | Computer vision | Unsafe behavior | Deep learning
مقاله انگلیسی
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