22 papers · ranked by Valyu relevance
Jing Yan, Tingliang Liu, Xinyu Ye, Qianzhen Jing + 2 more
'Chi-Hua Chen'] The advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCNN)…
Jun-Gi Jang, Chun Quan, Hyun Dong Lee, U Kang
How can we efficiently compress Convolutional Neural Network (CNN) while retaining their accuracy on classification tasks? Depthwise Separable Convolution (DSConv), which replaces a standard convolution with a depthwise convolution and a pointwise convolution, has been used for building lightweight architectures.…
Bin Sun, Jun Li, Ming Shao, Yun Fu
Deep learning has become popular in recent years primarily due to the powerful computing device such as GPUs. However, deploying these deep models to end-user devices, smart phones, or embedded systems with limited resources is challenging. To reduce the computation and memory costs, we propose a novel lightweight deep…
Wei Hua, Qili Chen, Wenbai Chen
Optimizing the structure of deep neural networks is essential in many applications. Especially in the object detection tasks of Unmanned Aerial Vehicles. Due to the constraints of the onboard platform, a more efficient network is required to meet practical demands. Nevertheless, existing lightweight detection networks…
Yuming Zhang, Chun-Chieh Lee, Jun-Wei Hsieh, Kuo‐Chin Fan
The development of lightweight object detectors is essential due to the limited computation resources. To reduce the computation cost, how to generate redundant features plays a significant role. This paper proposes a new lightweight Convolution method Cross-Stage Lightweight (CSL) Module, to generate redundant…
Liquan Zhao, Leilei Wang, Yanfei Jia, Ying Cui + 1 more
To improve accuracy of the MobileNet network, a new lightweight deep neural network is designed based on the MobileNetV2 network. Firstly, it modifies the network depth of MobileNetV2 to balance the image resolution, network width and depth to keep the gradient stable, which reduces the generation of gradient vanishing…
Yiheng Zhang, Ting Yao, Zhaofan Qiu, Tao Mei
Multi-scale learning frameworks have been regarded as a capable class of models to boost semantic segmentation. The problem nevertheless is not trivial especially for the real-world deployments, which often demand high efficiency in inference latency. In this paper, we thoroughly analyze the design of convolutional…
Hongwei Ning, Sheng Liu, Qifei Zhu, Teng Zhou
There are many rice diseases, which have very serious negative effects on rice growth and final yield. It is very important to identify the categories of rice diseases and control them. In the past, the identification of rice disease types was completely dependent on manual work, which required a high level of human…
Debesh Jha, Anis Yazidi, Michael A. Riegler, Dag Johansen + 2 more
'Håvard D. Johansen' 'Pål Halvorsen'] Abstract. Deep Neural Networks (DNNs) have become the de-facto standard in computer vision, as well as in many other pattern recognition tasks. A key drawback of DNNs is that the training phase can be very computationally expensive. Organizations or individuals that cannot afford…
Eunmi Shin, Chang D. Yoo, Kelvin K.L. Wong, Dhanjoo N. Ghista + 2 more
'Andrew W.H. Ip' 'Wenjun (Chris) Zhang'] The results obtained in the wafer test process are expressed as a wafer map and contain important information indicating whether each chip on the wafer is functioning normally. The defect patterns shown on the wafer map provide information about the process and equipment in…
Yangjun Pei, Mingyang Hou, Qi Han, Tengfei Weng + 4 more
'Guorong Chen' 'Jinyuan Liu' 'Chen Wu'] The classification method of steel surface defects based on deep learning provides a basis for quality control of industrial steel manufacturing. Due to a large number of interference in the steel production area and the limited computing resources of the edge equipment deployed…
Amit Kumar Manjhvar, Rajendra Parmula
Potato leaf diseases pose a serious challenge to global food security, often leading to considerable yield losses if not detected promptly. The growing maturity of deep learning has enabled automated, high-precision plant disease recognition, even on devices with limited computational resources. In this study, several…
Kai Han, Yunhe Wang, Chang Xu, Chunjing Xu + 2 more
—This paper introduces versatile filters to construct efficient convolutional neural networks that are widely used in various visual recognition tasks. Considering the demands of efficient deep learning techniques running on cost-effective hardware, a number of methods have been developed to learn compact neural…
Qingyuan Gong, Yu Liu, Liqiang Zhang, Renhe Liu
Visual place recognition (VPR) is a challenging task with the unbalance between enormous computational cost and high recognition performance. Thanks to the practical feature extraction ability of the lightweight convolution neural networks (CNNs) and the train-ability of the vector of locally aggregated descriptors…
Zachary Humphreys, Xenophon Evangelopoulos, Stavros Gerolymatos, Edward O. Pyzer-Knapp + 1 more
Graph neural networks have recently met huge success in various inference tasks including materials property prediction amongst many others. Nevertheless, having an inherently locally-based representation capacity as they do, global representation of materials' structures can only only be achieved by expanding the…
Authors not listed
Obtaining quantitative information about residence time behavior (i.e., the residence time distribution function) in realistic experimental systems is oftentimes experimentally challenging and numerically complex. The conventional way is to conduct very simple pulse or step tracer experiments or construct elaborate…
Joel Dapello, Tiago Marques, Martin Schrimpf, Franziska Geiger + 2 more
Current state-of-the-art object recognition models are largely based on convolutional neural network (CNN) architectures, which are loosely inspired by the primate visual system. However, these CNNs can be fooled by imperceptibly small, explicitly crafted perturbations, and struggle to recognize objects in corrupted…
Robin Gutzen, Grace W Lindsay
Convolutional Neural Networks (CNNs) trained for image recognition have demonstrated remarkable conceptual similarities to the primate ventral visual pathway, but their standard feedforward architectures lack the recurrent connections that are ubiquitous in visual cortex. Such recurrence is thought to underlie…
Sanket Kadulkar, Michael Howard, Thomas Truskett, Venkat Ganesan
We develop a convolutional neural network (CNN) model to predict the diffusivity of cations in nanoparticle-based electrolytes, and use it to identify the characteristics of morphologies which exhibit optimal transport properties. The ground truth data is obtained from kinetic Monte Carlo (kMC) simulations of cation…
Diego E. Galvez-Aranda, Tan Le Dinh, Utkarsh Vijay, Franco M. Zanotto + 1 more
The manufacturing process of Lithium-ion battery electrodes directly affects the practical properties of the cells, such as their performance, durability, and safety. While computational physics-based modeling has been proved as a useful method to produce insights on the manufacturing properties interdependencies as…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Xu Pan, Luis Gonzalo Sánchez Giraldo, Elif Kartal, Odelia Schwartz
We studied a local normalization paradigm, namely weighted normalization, that better reflects the current understanding of the brain. Specifically, the normalization weight is trainable, and has a more realistic surround pool selection. Weighted normalization outperformed other normalizations in image classification…