NAS-HRIS: Automatic Design and Architecture Search of Neural Network for Semantic Segmentation in Remote Sensing Images

Zhang, Mingwei; Jing, Weipeng; Lin, Jingbo; Fang, Nengzhen; Wei, Wei; Woźniak, Marcin; Damaševičius, Robertas

doi:10.3390/s20185292

Cited by 26 publications

(10 citation statements)

References 33 publications

(38 reference statements)

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“…is system involves various businesses related to 3D soft furnishings, including design results and conceptual drawings under design concepts, which are related to the Complexity original wisdom of the creators, which requires this system to have good data security. erefore, this system has clear permission restrictions for users, set up by limited setting of system roles in the system, and then configuring different permission ranges for each role to ensure that only the creator can view data related to the design work [24][25][26]. Software development cannot be separated from the use of the Internet; the system's web content management module includes two aspects of consulting information and product information.…”

Section: Rapid Design System Design For Arts and Crafts With 3dmentioning

confidence: 99%

Optimization of the Rapid Design System for Arts and Crafts Based on Big Data and 3D Technology

Zhou

2021

Complexity

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In this paper, to solve the problem of slow design of arts and crafts and to improve design efficiency and aesthetics, the existing big data and 3D technology are used to conduct an in-depth analysis of the optimization of the rapid design system of arts and crafts machine salt baking. In the system requirement analysis, the functional modules of this system are identified as nine functional modules such as design terminology management system and external information import function according to the actual usage requirements. In the system design, the overall structure design, database design, and functional module design of the system are comprehensively elaborated, and the key issues such as 3D display and home layout generation algorithm based on reinforcement learning are analyzed and designed. In the implementation part of the system, the overall construction of the system and the composition of functional modules are introduced in detail and the main functional modules of the system are presented with interface diagrams. In the system implementation part, the overall system construction and functional module composition are introduced in detail, the main functional modules of the system are shown with interface diagrams, codes, and algorithms, and the specific implementation process of 3D display and soft layout algorithms are also explained in detail. The process of Surface Mount Technology (SMT) big data processing and analysis is designed, and the design of SMT production line data collection scheme and real-time data processing architecture is completed. Based on the characteristics of SMT production line data, the K-means algorithm is used to detect data outliers and verify the accuracy of the method; also, the Spark-based association rule printing parameter recommendation model is designed, and the efficiency of the Apriori algorithm is significantly improved by parallelization.

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Section: Rapid Design System Design For Arts and Crafts With 3dmentioning

confidence: 99%

Optimization of the Rapid Design System for Arts and Crafts Based on Big Data and 3D Technology

Zhou

2021

Complexity

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“…Various object detection tasks have achieved leap-forward progress. Later, some optimization methods in association with neural networks appeared [46][47][48]. At present, neural networks have a wide range of applications [49][50][51][52][53].…”

Section: Introductionmentioning

confidence: 99%

A Review of Intelligent Driving Pedestrian Detection Based on Deep Learning

Tian

Yong

Wang

et al. 2021

Computational Intelligence and Neuroscience

Self Cite

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Pedestrian detection is a specific application of object detection. Compared with general object detection, it shows similarities and unique characteristics. In addition, it has important application value in the fields of intelligent driving and security monitoring. In recent years, with the rapid development of deep learning, pedestrian detection technology has also made great progress. However, there still exists a huge gap between it and human perception. Meanwhile, there are still a lot of problems, and there remains a lot of room for research. Regarding the application of pedestrian detection in intelligent driving technology, it is of necessity to ensure its real-time performance. Additionally, it is necessary to lighten the model while ensuring detection accuracy. This paper first briefly describes the development process of pedestrian detection and then concentrates on summarizing the research results of pedestrian detection technology in the deep learning stage. Subsequently, by summarizing the pedestrian detection dataset and evaluation criteria, the core issues of the current development of pedestrian detection are analyzed. Finally, the next possible development direction of pedestrian detection technology is explained at the end of the paper.

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“…The proposed neural network model or modifications can be engaged in problems such as remote image segmentation [ 65 , 66 ], medicine [ 67 , 68 ], faults detection [ 69 , 70 ], and others.…”

Section: Discussionmentioning

confidence: 99%

Segmentation of Drilled Holes in Texture Wooden Furniture Panels Using Deep Neural Network

Augustauskas

Lipnickas

Surgailis³

2021

Sensors

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Drilling operations are an essential part of furniture from MDF laminated boards required for product assembly. Faults in the process might introduce adverse effects to the furniture. Inspection of the drilling quality can be challenging due to a big variety of board surface textures, dust, or woodchips in the manufacturing process, milling cutouts, and other kinds of defects. Intelligent computer vision methods can be engaged for global contextual analysis with local information attention for automated object detection and segmentation. In this paper, we propose blind and through drilled holes segmentation on textured wooden furniture panel images using the UNet encoder-decoder modifications enhanced with residual connections, atrous spatial pyramid pooling, squeeze and excitation module, and CoordConv layers for better segmentation performance. We show that even a lightweight architecture is capable to perform on a range of complex textures and is able to distinguish the holes drilling operations’ semantical information from the rest of the furniture board and conveyor context. The proposed model configurations yield better results in more complex cases with a not significant or small bump in processing time. Experimental results demonstrate that our best-proposed solution achieves a Dice score of up to 97.89% compared to the baseline U-Net model’s Dice score of 94.50%. Statistical, visual, and computational properties of each convolutional neural network architecture are addressed.

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NAS-HRIS: Automatic Design and Architecture Search of Neural Network for Semantic Segmentation in Remote Sensing Images

Cited by 26 publications

References 33 publications

Optimization of the Rapid Design System for Arts and Crafts Based on Big Data and 3D Technology

Optimization of the Rapid Design System for Arts and Crafts Based on Big Data and 3D Technology

A Review of Intelligent Driving Pedestrian Detection Based on Deep Learning

Segmentation of Drilled Holes in Texture Wooden Furniture Panels Using Deep Neural Network

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