Deep learning (DL) based instance segmentation has attracted a growing research interest in the scientific community to tackle precision agriculture problems over the past few years. However, accurate crop detection a...
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Deep learning (DL) based instance segmentation has attracted a growing research interest in the scientific community to tackle precision agriculture problems over the past few years. However, accurate crop detection and localization in complex environments pose a significant challenge. Instance segmentation is considered as a promising DL technique that expands on object detection to perform pixel-wise image instance segmentation and address pattern recognition problems efficiently. In this review, we identify 77 relevant studies on DL-based instance segmentation implementations in agriculture and thoroughly investigate them from the following perspectives: i) the specific architecture employed;ii) the data type and availability, the data annotation process and the data pre-processing techniques;iii) the performance metrics used;and iv) hardware, inference time and GPU requirements. Our findings indicate that crop detection (48 papers) constitutes a fundamental task in a DLbased instance segmentation pipeline to enable crop growth monitoring (19 papers) and plant health analysis (10 papers). Among them, 6 papers reported robotic manipulation and other related automation tasks. Based on our findings we can conclude that there is a significant trend towards two-stage DL-based instance segmentation models i.e., Mask R-CNN baseline and customized architectures (69 papers). Limitations and challenges, such as availability of benchmark crop datasets, open-source codes for semi-automatic annotation tools, technical requirements and opportunities for future research are discussed.
Recycling facilities are often challenged by the demanding task of waste segregation, a process traditionally requiring substantial time and manpower, and lacking efficient automated solutions. Addressing this, our st...
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Geometric Algebra (GA) has proven to be an advanced language for mathematics, physics, computer science, and engineering. This review presents a comprehensive study of works on Quaternion Algebra and GA applications i...
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Geometric Algebra (GA) has proven to be an advanced language for mathematics, physics, computer science, and engineering. This review presents a comprehensive study of works on Quaternion Algebra and GA applications in computer science and engineering from 1995 to 2020. After a brief introduction of GA, the applications of GA are reviewed across many fields. We discuss the characteristics of the applications of GA to various problems of computer science and engineering. In addition, the challenges and prospects of various applications proposed by many researchers are analyzed. We analyze the developments using GA in imageprocessing, computer vision, neurocomputing, quantum computing, robot modeling, control, and tracking, as well as improvement of computer hardware performance. We believe that up to now GA has proven to be a powerful geometric language for a variety of applications. Furthermore, there is evidence that this is the appropriate geometric language to tackle a variety of existing problems and that consequently, step-by-step GA-based algorithms should continue to be further developed. We also believe that this extensive review will guide and encourage researchers to continue the advancement of geometric computing for intelligent machines.
Pixel-level sea-land segmentation on high-resolution remote sensing images is a basic task in remote sensing applications and is of great significance for coastline extraction and near-shore marine target detection. T...
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Weather conditions play a crucial role in both daily human activities and industrial operations. For example, recognizing different weather patterns is critical for outdoor automation systems. With the development of ...
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The universal transmission of pandemic COVID-19 (Coronavirus) causes an immediate need to commit in the fight across the whole human population. The emergencies for human health care are limited for this abrupt outbre...
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The universal transmission of pandemic COVID-19 (Coronavirus) causes an immediate need to commit in the fight across the whole human population. The emergencies for human health care are limited for this abrupt outbreak and abandoned environment. In this situation, inventive automation like computer vision (machine learning, deep learning, artificial intelligence), medical imaging (computed tomography, X-Ray) has developed an encouraging solution against COVID-19. In recent months, different techniques using imageprocessing are done by various researchers. In this paper, a major review on image acquisition, segmentation, diagnosis, avoidance, and management are presented. An analytical comparison of the various proposed algorithm by researchers for coronavirus has been carried out. Also, challenges and motivation for research in the future to deal with coronavirus are indicated. The clinical impact and use of computer vision and deep learning were discussed and we hope that dermatologists may have better understanding of these areas from the study.
Facial image-based kinship verification is one of the challenging tasks in computer vision. It has many potential applications, such as human trafficking, studying human genetics, generating family maps, family photo ...
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ISBN:
(纸本)9783031500688;9783031500695
Facial image-based kinship verification is one of the challenging tasks in computer vision. It has many potential applications, such as human trafficking, studying human genetics, generating family maps, family photo albums, etc. Therefore, we propose a deep feature learning method (DFLKV) which can extract more discriminative features for kinship verification. For a pair of facial images, we firstly design a network with multi-scale channel attention for the features extraction;then, select four methods for feature fusion;finally, infer kinship based on the fused features. We jointly adopt the contrastive loss and the binary cross-entropy loss to compute matching degree for paired samples. The experimental results on four widely used datasets KinFaceW-I, KinFaceW-ii, Cornell KinFace and TS KinFace to validate the effectiveness of our approach.
This study presents an innovative approach to animal classification and recognition utilizing machine learning and deep learning methodologies. Leveraging advanced algorithms, the proposed system achieves remarkable a...
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Thanks to the emergence and continued devel-opment of machine learning, particularly deep learning, the research on visual question and answer, also known as VQA, has advanced dramatically, with great theoretical rese...
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object detection based on event vision has been a dynamically growing field in computer vision for the last 16 years. In this work, we create multiple channels from a single event camera and propose an event fusion me...
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ISBN:
(数字)9798331506520
ISBN:
(纸本)9798331506537
object detection based on event vision has been a dynamically growing field in computer vision for the last 16 years. In this work, we create multiple channels from a single event camera and propose an event fusion method (EFM) to enhance object detection in event-based vision systems. Each channel uses a different accumulation buffer to collect events from the event camera. We implement YOLOv7 for object detection, followed by a fusion algorithm. Our multichannel approach outperforms single-channel-based object detection by 0.7% in mean Average Precision (mAP) for detection overlapping ground truth with IOU = 0.5.
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