Search Results - "depthwise-pointwise convolution"

Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode by Ye, Tao, Wang, Baocheng, Song, Ping, Li, Juan

“…Many accidents happen under shunting mode when the speed of a train is below 45 km/h. In this mode, train attendants observe the railway condition ahead using…”
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Soil-MobiNet: A Convolutional Neural Network Model Base Soil Classification to Determine Soil Morphology and Its Geospatial Location by Gyasi, Emmanuel Kwabena, Purushotham, Swarnalatha

“…Scholars have classified soil to understand its complex and diverse characteristics. The current trend of precision agricultural technology demands a change in…”
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Real-World Railway Traffic Detection Based on Faster Better Network by Li, Juan, Zhou, Fuqiang, Ye, Tao

“…Detection of railway shape and dangerous obstacles plays a critical role in the auxiliary driving of the train. Speed and accuracy are both of great…”
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Plant leaf disease detection and classification using a lightweight DPCNet model by V, Gnanaprakash, Raju, Manu, N, Sathishkumar, D, Preethi, N, Saranya, R, Ezilarasan M

“…Plant disease is one of the major causes for an overall decrease in yield and reduces the profit from the farming segment. Convolutional Neural Networks (CNN)…”
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Efficient tick-shape networks of full-residual point-depth-point blocks for image classification by Nguyen, Thanh Tuan, Nguyen, Thanh Phuong

“…Light-weight convolutional neural networks (CNNs) are crucial for deploying computer vision applications in mobile devices thanks to their compact models,…”
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Nettop: A light-weight network of orthogonal-plane features for image recognition by Nguyen, Thanh Tuan, Nguyen, Thanh Phuong

“…In the current light-weight CNN-based networks, convolutional operators are principally utilized to extract feature maps for image representation. However,…”
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A light-weight backbone to adapt with extracting grouped dilation features by Nguyen, Thanh Tuan, Pham, Hoang Anh, Nguyen, Thanh Phuong

“…Addressing grouped dilation features (GDFs) improved the learning ability of MobileNetV1 in image representation. However, the computational complexity is…”
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