scholarly journals Random CNN Structure – Tool to Increase Generalization Ability in Deep Learning

2021 ◽  
Author(s):  
Bartosz Swiderski ◽  
Stanislaw Osowski ◽  
Grzegorz Gwardys ◽  
Jaroslaw Kurek ◽  
Monika Slowinska ◽  
...  
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Quality Measures ◽  
Activation Function ◽  
Small Population ◽  
Generalization Capability ◽  
Average Quality ◽  
Novel Approach ◽  
Learning Data ◽  
Nonlinear Activation Function

Abstract The paper presents a novel approach to designing the CNN structure of improved generalization capability in the presence of a small population of learning data. In contrast to the classical methods for building CNN, we propose to introduce some randomness in the choice of layers with a different type of nonlinear activation function. Image processing in these layers is performed using either the ReLU or the softplus function. This choice is random. The randomness introduced into the network structure can be interpreted as a special form of regularization. Experiments performed in the recognition of images belonging to either melanoma or non-melanoma cases have shown a significant improvement in the average quality measures, such as the accuracy, sensitivity, precision, and the area under the ROC curve.

scholarly journals A Novel YOLOv3 Algorithm-Based Deep Learning Approach for Waste Segregation: Towards Smart Waste Management

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 14
Author(s):  
Saurav Kumar ◽  
Drishti Yadav ◽  
Himanshu Gupta ◽  
Om Prakash Verma ◽  
Irshad Ahmad Ansari ◽  
...  
Keyword(s):  
Deep Learning ◽  
Waste Management ◽  
Organic Waste ◽  
Learning Strategy ◽  
Smart Cities ◽  
Learning Approach ◽  
Generalization Capability ◽  
Novel Approach ◽  
Waste Segregation ◽  
Glass Metal

The colossal increase in environmental pollution and degradation, resulting in ecological imbalance, is an eye-catching concern in the contemporary era. Moreover, the proliferation in the development of smart cities across the globe necessitates the emergence of a robust smart waste management system for proper waste segregation based on its biodegradability. The present work investigates a novel approach for waste segregation for its effective recycling and disposal by utilizing a deep learning strategy. The YOLOv3 algorithm has been utilized in the Darknet neural network framework to train a self-made dataset. The network has been trained for 6 object classes (namely: cardboard, glass, metal, paper, plastic and organic waste). Moreover, for comparative assessment, the detection task has also been performed using YOLOv3-tiny to validate the competence of the YOLOv3 algorithm. The experimental results demonstrate that the proposed YOLOv3 methodology yields satisfactory generalization capability for all the classes with a variety of waste items.

Deep Learning-Based Image Processing for Whitecaps on the Ocean Surface

2020 ◽  
Vol 76 (2) ◽  
pp. I_163-I_168
Author(s):  
Yukun WANG ◽  
Yuji SUGIHARA ◽  
Xianting ZHAO ◽  
Haruki NAKASHIMA ◽  
Osama ELJAMAL
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Ocean Surface

scholarly journals Deep Learning for Transient Image Reconstruction from ToF Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1962
Author(s):  
Enrico Buratto ◽  
Adriano Simonetto ◽  
Gianluca Agresti ◽  
Henrik Schäfer ◽  
Pietro Zanuttigh
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Light Response ◽  
Real Data ◽  
Depth Image ◽  
Learning Approach ◽  
Multiple Reflections ◽  
Noisy Input ◽  
Novel Approach ◽  
Incoming Light

In this work, we propose a novel approach for correcting multi-path interference (MPI) in Time-of-Flight (ToF) cameras by estimating the direct and global components of the incoming light. MPI is an error source linked to the multiple reflections of light inside a scene; each sensor pixel receives information coming from different light paths which generally leads to an overestimation of the depth. We introduce a novel deep learning approach, which estimates the structure of the time-dependent scene impulse response and from it recovers a depth image with a reduced amount of MPI. The model consists of two main blocks: a predictive model that learns a compact encoded representation of the backscattering vector from the noisy input data and a fixed backscattering model which translates the encoded representation into the high dimensional light response. Experimental results on real data show the effectiveness of the proposed approach, which reaches state-of-the-art performances.

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

Sign in / Sign up

Export Citation Format

Share Document