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نتیجه جستجو - segmentation

تعداد مقالات یافته شده: 206
ردیف عنوان نوع
1 ChickenNet - an end-to-end approach for plumage condition assessment of laying hens in commercial farms using computer vision
ChickenNet - یک رویکرد انتها به انتها برای ارزیابی وضعیت پرهای مرغ های تخمگذار در مزارع تجاری با استفاده از بینایی کامپیوتر-2022
Regular plumage condition assessment in laying hens is essential to monitor the hens’ welfare status and to detect the occurrence of feather pecking activities. However, in commercial farms this is a labor-intensive, manual task. This study proposes a novel approach for automated plumage condition assessment using com- puter vision and deep learning. It presents ChickenNet, an end-to-end convolutional neural network that detects hens and simultaneously predicts a plumage condition score for each detected hen. To investigate the effect of input image characteristics, the method was evaluated using images with and without depth information in resolutions of 384 × 384, 512 × 512, 896 × 896 and 1216 × 1216 pixels. Further, to determine the impact of subjective human annotations, plumage condition predictions were compared to manual assessments of one observer and to matching annotations of two observers. Among all tested settings, performance metrics based on matching manual annotations of two observers were equal or better than the ones based on annotations of a single observer. The best result obtained among all tested configurations was a mean average precision (mAP) of 98.02% for hen detection while 91.83% of the plumage condition scores were predicted correctly. Moreover, it was revealed that performance of hen detection and plumage condition assessment of ChickenNet was not generally enhanced by depth information. Increasing image resolutions improved plumage assessment up to a resolution of 896 × 896 pixels, while high detection accuracies (mAP > 0.96) could already be achieved using lower resolutions. The results indicate that ChickenNet provides a sufficient basis for automated monitoring of plumage conditions in commercial laying hen farms.
keywords: طیور | ارزیابی پر و بال | بینایی کامپیوتر | یادگیری عمیق | تقسیم بندی نمونه | Poultry | Plumage assessment | Computer vision | Deep learning | Instance segmentation
مقاله انگلیسی
2 Semantic Riverscapes: Perception and evaluation of linear landscapes from oblique imagery using computer vision
مناظر معنایی رودخانه: درک و ارزیابی مناظر خطی از تصاویر مایل با استفاده از بینایی کامپیوتری-2022
Traditional approaches for visual perception and evaluation of river landscapes adopt on-site surveys or as- sessments through photographs. The former is expensive, hindering large-scale analyses, and it is conducted only on street-level or top-down imagery. The latter only reflects the subjective perception and also entails a laborious process. Addressing these challenges, this study proposes an alternative: a novel workflow for visual analysis of urban river landscapes by combining unmanned aerial vehicle (UAV) oblique photography with computer vision (CV) and virtual reality (VR). The approach is demonstrated with an experiment on a section of the Grand Canal in China where UAV oblique panoramic imagery has been processed using semantic segmentation for visual evaluation with an index system we designed. Concurrent surveys, immersive and non-immersive VR, are used to evaluate these photos, with a total of 111 participants expressing their perceptions across multiple dimensions. Then, the relationship between the people’s subjective visual perception and the river landscape environment as seen by computers has been established. The results suggest that using this approach, rivers and surrounding landscapes can be analyzed automatically and efficiently, and the mean pixel accuracy (MPA) of the developed model is 90%, which advances state of the art. The results of this study can benefit urban planners in formulating riverside development policies, analyzing the perception of plans for a future scenario before an area is rede- veloped, and the method can also aid relevant parties in having a macro understanding of the overall situation of the river as a basis for follow-up research. Due to simplicity, accuracy and effectiveness, this workflow is transferable and cost-effective for large-scale investigations of riverscapes and linear heritage. We openly release Semantic Riverscapes—the dataset we collected and processed, bridging another gap in the field.
keywords: ریورساید | باز کردن داده ها | GeoAI | بررسی های هوایی | هواپیماهای بدون سرنشین | واقعیت مجازی | Riverside | Open data | GeoAI | Aerial surveys | Drones | Virtual reality
مقاله انگلیسی
3 Assessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data
ارزیابی شرایط زهکشی سطحی در مقیاس خیابان و محله: یک روش دید کامپیوتری و جهت جریان اعمال شده به داده های لیدار-2022
Surface drainage at the neighborhood and street scales plays an important role in conveying stormwater and mitigating urban flooding. Surface drainage at the local scale is often ignored due to the lack of up-to-date fine- scale topographical information. This paper addresses this issue by providing a novel method for evaluating surface drainage at the neighborhood and street scales based on mobile lidar (light detection and ranging) measurements. The developed method derives topographical properties and runoff accumulation by applying a semantic segmentation (SS) model (a computer vision technique) and a flow direction model (a hydrology technique) to lidar data. Fifty lidar images representing 50 street blocks were used to train, validate, and test the SS model. Based on the test dataset, the SS model has 80.3% IoU and 88.5% accuracy. The results suggest that the proposed method can effectively evaluate surface drainage conditions at both the neighborhood and street scales and identify problematic low points that could be susceptible to water ponding. Municipalities and property owners can use this information to take targeted corrective maintenance actions.
keywords: تقسیم بندی معنایی | جهت جریان | لیدار موبایل | زهکشی سطحی | زیرساخت های زهکشی | Semantic segmentation | Flow direction | Mobile lidar | Surface drainage | Drainage infrastructure
مقاله انگلیسی
4 Computer vision-based illumination-robust and multi-point simultaneous structural displacement measuring method
روش اندازه گیری جابجایی ساختاری همزمان با روشنایی مبتنی بر بینایی کامپیوتری-2022
Computer vision-based techniques for structural displacement measurement are rapidly becoming popular in civil structural engineering. However, most existing computer vision-based displace- ment measurement methods require man-made targets for object matching or tracking, besides usually the measurement accuracies are seriously sensitive to the ambient illumination variations. A computer vision-based illumination robust and multi-point simultaneous measuring method is proposed for structural displacement measurements. The method consists of two part, one is for segmenting the beam body from its background, the segmentation is perfectly carried out by fully convolutional network (FCN) and conditional random field (CRF); another is digital image cor- relation (DIC)-based displacement measurement. A simply supported beam is built in laboratory. The accuracy and illumination robustness are verified through three groups of elaborately designed experiments. Due to the exploitation of FCN and CRF for pixel-wise segmentation, numbers of locations along with the segmented beam body can be chosen and measured simul- taneously. It is verified that the method is illumination robust since the displacement measure- ments are with the smallest fluctuations to the illumination variations. The proposed method does not require any man-made targets attached on the structure, but because of the exploitation of DIC in displacement measurement, the regions centered on the measuring points need to have texture feature.
keywords: پایش سلامت سازه | اندازه گیری جابجایی | بینایی کامپیوتر | یادگیری عمیق | تقسیم بندی شی | همبستگی تصویر دیجیتال | Structural health monitoring | Displacement measurement | Computer vision | Deep learning | Object segmentation | Digital image correlation
مقاله انگلیسی
5 Detection of loosening angle for mark bolted joints with computer vision and geometric imaging
تشخیص زاویه شل شدن اتصالات پیچ شده با بینایی ماشین و تصویربرداری هندسی-2022
Mark bars drawn on the surfaces of bolted joints are widely used to indicate the severity of loosening. The automatic and accurate determination of the loosening angle of mark bolted joints is a challenging issue that has not been investigated previously. This determination will release workers from heavy workloads. This study proposes an automated method for detecting the loosening angle of mark bolted joints by integrating computer vision and geometric imaging theory. This novel method contained three integrated modules. The first module used a Keypoint Regional Convolutional Neural Network (Keypoint-RCNN)-based deep learning algorithm to detect five keypoints and locate the region of interest (RoI). The second module recognised the mark ellipse and mark points using the transformation of the five detected keypoints and several image processing technologies such as dilation and expansion algorithms, a skeleton algorithm, and the least square method. In the last module, according to the geometric imaging theory, we derived a precise expression to calculate the loosening angle using the information for the mark points and mark ellipse. In lab-scale and real-scale environments, the average relative detection error was only 3.5%. This indicated that our method could accurately calculate the loosening angles of marked bolted joints even when the images were captured from an arbitrary view. In the future, some segmentation algorithms based on deep learning, distortion correction, accurate angle and length measuring instruments, and advanced transformation methods can be applied to further improve detection accuracy.
keywords: Mark bolted joint | Loosening detection | Keypoint-RCNN | Image processing | Geometric imaging
مقاله انگلیسی
6 Survey on deep learning based computer vision for sonar imagery
مروری بر بینایی کامپیوتری مبتنی بر یادگیری عمیق برای تصاویر سونار-2022
Research on the automatic analysis of sonar images has focused on classical, i.e. non deep learning based, approaches for a long time. Over the past 15 years, however, the application of deep learning in this research field has constantly grown. This paper gives a broad overview of past and current research involving deep learning for feature extraction, classification, detection and segmentation of sidescan and synthetic aperture sonar imagery. Most research in this field has been directed towards the investigation of convolutional neural networks (CNN) for feature extraction and classification tasks, with the result that even small CNNs with up to four layers outperform conventional methods.
The purpose of this work is twofold. On one hand, due to the quick development of deep learning it serves as an introduction for researchers, either just starting their work in this specific field or working on classical methods for the past years, and helps them to learn about the recent achievements. On the other hand, our main goal is to guide further research in this field by identifying main research gaps to bridge. We propose to leverage the research in this field by combining available data into an open source dataset as well as carrying out comparative studies on developed deep learning methods.
keywords: یادگیری عمیق | تصویربرداری سوناری | کامپیوتری | تشخیص خودکار هدف | Statusquoreview | Deeplearning | Sonarimagery | Computervision | Automatictargetrecognition | Statusquoreview
مقاله انگلیسی
7 Automated bridge surface crack detection and segmentation using computer vision-based deep learning model
تشخیص و تقسیم خودکار ترک سطح پل با استفاده از مدل یادگیری عمیق مبتنی بر بینایی کامپیوتری-2022
Bridge maintenance will become a widespread trend in the engineering industry as the number of bridges grows and time passes. Cracking is a common problem in bridges with concrete structures. Allowing it to expand will result in significant economic losses and accident risks This paper proposed an automatic detection and segmentation method of bridge surface cracks based on computer vision deep learning models. First, a bridge surface crack detection and segmentation dataset was established. Then, according to the characteristics of the bridge, we improved the You Only Look Once (YOLO) algorithm for bridge surface crack detection. The improved algorithm was defined as CR-YOLO, which can identify cracks and their approximate locations from multi-object images. Subsequently, the PSPNet algorithm was improved to segment the bridge cracks from the non-crack regions to avoid the visual interference of the detection algorithm. Finally, we deployed the proposed bridge crack detection and segmentation algorithm in an edge device. The experimental results show that our method outperforms other baseline methods in generic evaluation metrics and has advantages in Model Size(MS) and Frame Per Second (FPS).
keywords: Bridge crack Crack detection | Crack segmentation | Deep learning | Computer vision
مقاله انگلیسی
8 GAFL: Global adaptive filtering layer for computer vision
GAFL: لایه فیلتر تطبیقی جهانی برای بینایی کامپیوتر-2022
We devise a universal global adaptive filtering layer, GAFL, capable of ‘‘learning’’ optimal frequency filter for each image in a dataset together with the weights of the base neural network that performs some computer vision task. The proposed approach takes the source image in the spatial domain, selects the best frequencies in the Fourier domain for the benefit of the global task, and prepends the inverse-transform image to the main neural network for a joint training. Remarkably, such a simple add-on layer, capable of optimizing the frequency content of an input for a specific task, dramatically improves the performance of the main network regardless of its design. We observe that the light networks gain a noticeable boost in the performance metrics; whereas, the training of the heavy ones converges faster when GAFL is prepended to the main architecture. We showcase the performance of the layer in four classical computer vision tasks: classification, segmentation, denoising, and erasing, considering popular natural and medical data benchmarks.
keywords: Adaptive neural layer | Efficient training | Fourier filtering
مقاله انگلیسی
9 Co-segmentation inspired attention module for video-based computer vision tasks
ماژول توجه الهام گرفته از تقسیم بندی مشترک برای وظایف بینایی کامپیوتری مبتنی بر ویدئو-2022
Video-based computer vision tasks can benefit from estimation of the salient regions and interactions between those regions. Traditionally, this has been done by identifying the object regions in the images by utilizing pre-trained models to perform object detection, object segmentation and/or object pose estimation. Although using pre-trained models is a viable approach, it has several limitations in the need for an exhaustive annotation of object categories, a possible domain gap between datasets and a bias that is typically present in pre-trained models. In this work, we propose to utilize the common rationale that a sequence of video frames capture a set of common objects and interactions between them, thus a notion of co-segmentation between the video frame features may equip the model with the ability to automatically focus on task-specific salient regions and improve the underlying task’s performance in an end-to-end manner. In this regard, we propose a generic module called ‘‘Co-Segmentation inspired Attention Module’’ (COSAM) that can be plugged in to any CNN model to promote the notion of co-segmentation based attention among a sequence of video frame features. We show the application of COSAM in three video-based tasks namely: (1) Video-based person re-ID, (2) Video captioning, & (3) Video action classification and demonstrate that COSAM is able to capture the task-specific salient regions in video frames, thus leading to notable performance improvements along with interpretable attention maps for a variety of video-based vision tasks, with possible application to other video-based vision tasks as well.
keywords: توجه | تقسیم بندی مشترک | شناسه شخص | زیرنویس ویدیویی | طبقه بندی ویدیویی | Attention | Co-segmentation | Personre-ID | Video-captioning | Video classification
مقاله انگلیسی
10 Computer vision technique for freshness estimation from segmented eye of fish image
تکنیک بینایی کامپیوتری برای تخمین تازگی از چشم تقسیم شده تصویر ماهی-2022
Preserving the quality of fish is a challenging task. Several different cooling methods and materials are used during their storage, transportation purpose. These are responsible factors that decide the freshness of a post harvested fish. In this proposed algorithm, a computer vision-based technique is developed to predict the freshness level of fish from its image. Eyes of the fish are considered as the region of interest, as a good corre- lation has been observed between the colour of the eye and different duration of storage day. It is segmented from the image of a fish sample and then a strategic framework is used for extraction of the discriminatory features. These extracted features show a degradation pattern which acts as an indicative parameter to determine the level of freshness of sample of fish. The proposed method provides a recognition accuracy of 96.67%. The experimental results indicate that this is an efficient and non-destructive methodology for detecting the fish freshness. The high accuracy of freshness detection and low computation time makes this non-destructive methodology efficient for real-world usage in the fish industry and market.
keywords: استخراج ویژگی | چشم ماهی | تکنیک های پردازش تصویر | سطح تازگی | تقسیم بندی | Feature extraction | Fish eye | Image processing techniques | Level of freshness | Segmentation
مقاله انگلیسی
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