Research on Semantic Segmentation of High-resolution Remote Sensing Image Based on Full Convolutional Neural Network

Fu, Xiaomeng; Qu, Hanbing

doi:10.1109/isape.2018.8634106

Cited by 22 publications

(9 citation statements)

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“…Compared with the classic FCN model and U-Net model, the proposed method exhibited significantly higher accuracy and could effectively classify the pixels of rice seedlings, background, and weeds in rice field images. To control weeds in the early stages of growth, Fu et al [ 141 ] proposed a segmentation method based on FCNs for high-resolution remote sensing images. On the basis of the VGG16 CNN model, a pretrained FCN was used to fine-tune the object data.…”

Section: Weed Detection and Identification Methods Based On Deep Learningmentioning

confidence: 99%

Review of Weed Detection Methods Based on Computer Vision

Chen

Zhao

et al. 2021

Sensors

148

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Weeds are one of the most important factors affecting agricultural production. The waste and pollution of farmland ecological environment caused by full-coverage chemical herbicide spraying are becoming increasingly evident. With the continuous improvement in the agricultural production level, accurately distinguishing crops from weeds and achieving precise spraying only for weeds are important. However, precise spraying depends on accurately identifying and locating weeds and crops. In recent years, some scholars have used various computer vision methods to achieve this purpose. This review elaborates the two aspects of using traditional image-processing methods and deep learning-based methods to solve weed detection problems. It provides an overview of various methods for weed detection in recent years, analyzes the advantages and disadvantages of existing methods, and introduces several related plant leaves, weed datasets, and weeding machinery. Lastly, the problems and difficulties of the existing weed detection methods are analyzed, and the development trend of future research is prospected.

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Section: Weed Detection and Identification Methods Based On Deep Learningmentioning

confidence: 99%

Review of Weed Detection Methods Based on Computer Vision

Chen

Zhao

et al. 2021

Sensors

148

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“…It saves a lot of time and computation as instead of producing the learning process from scratches, we begin with patterns obtained from solving related tasks. There are several models trained from before such as MobileNetV2 [52], VGG16 [53], InceptionV3 [54], ResNet50 [55]. They are trained on the ImageNet dataset that is containing almost 14 million images.…”

Section: -Model Trainingmentioning

confidence: 99%

Real-Time Monitoring of COVID-19 SOP in Public Gathering Using Deep Learning Technique

et al. 2021

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Crowd management has attracted serious attention under the prevailing pandemic conditions of COVID-19, emphasizing that sick persons do not become a source of virus transmission. World Health Organization (WHO) guidelines include maintaining a safe distance and wearing a mask in gatherings as part of standard operating procedures (SOP), considered thus far the most effective preventive measures to protect against COVID-19. Several methods and strategies have been used to construct various face detection and social distance detection models. In this paper, a deep learning model is presented to detect people without masks and those not keeping a safe distance to contain the virus. It also counts individuals who violate the SOP. The proposed model employs the Single Shot Multi-box Detector as a feature extractor, followed by Spatial Pyramid Pooling (SPP) to integrate the extracted features to improve the model's detecting capabilities. The MobilenetV2 architecture as a framework for the classifier makes the model highly light, fast, and computationally efficient, allowing it to be employed in embedded devices to do real-time mask and social distance detection, which is the sole objective of this research. This paper's technique yields an accuracy score of 99% and reduces the loss to 0.04%. Doi: 10.28991/esj-2021-SPER-14 Full Text: PDF

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“…At the same time, the intermediate pooling layer information is combined to generate the image prediction segmentation map. Reference [26] combined the spectral recognition index of blue-board houses and bare soil.…”

Section: Related Workmentioning

confidence: 99%

Remote Sensing Image Semantic Segmentation Algorithm Based on Improved ENet Network

Wang

2021

Scientific Programming

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A remote sensing image semantic segmentation algorithm based on improved ENet network is proposed to improve the accuracy of segmentation. First, dilated convolution and decomposition convolution are introduced in the coding stage. They are used in conjunction with ordinary convolution to increase the receptive field of the model. Each convolution output contains a larger range of image information. Second, in the decoding stage, the image information of different scales is obtained through the upsampling operation and then through the compression, excitation, and reweighting operations of the Squeeze and Excitation (SE) module. The weight of each feature channel is recalibrated to improve the accuracy of the network. Finally, the Softmax activation function and the Argmax function are used to obtain the final segmentation result. Experiments show that our algorithm can significantly improve the accuracy of remote sensing image semantic segmentation.

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Research on Semantic Segmentation of High-resolution Remote Sensing Image Based on Full Convolutional Neural Network

Cited by 22 publications

References 1 publication

Review of Weed Detection Methods Based on Computer Vision

Review of Weed Detection Methods Based on Computer Vision

Real-Time Monitoring of COVID-19 SOP in Public Gathering Using Deep Learning Technique

Remote Sensing Image Semantic Segmentation Algorithm Based on Improved ENet Network

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