scholarly journals A Method of Fast Segmentation for Banana Stalk Exploited Lightweight Multi-Feature Fusion Deep Neural Network

Machines ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 66
Author(s):  
Tianci Chen ◽  
Rihong Zhang ◽  
Lixue Zhu ◽  
Shiang Zhang ◽  
Xiaomin Li
Keyword(s):  
Neural Network ◽  
Execution Time ◽  
Deep Neural Network ◽  
Feature Fusion ◽  
Frame Rate ◽  
Light Conditions ◽  
Segmentation Method ◽  
Segmentation Accuracy ◽  
Automatic Picking ◽  
Changing Light

In an orchard environment with a complex background and changing light conditions, the banana stalk, fruit, branches, and leaves are very similar in color. The fast and accurate detection and segmentation of a banana stalk are crucial to realize the automatic picking using a banana picking robot. In this paper, a banana stalk segmentation method based on a lightweight multi-feature fusion deep neural network (MFN) is proposed. The proposed network is mainly composed of encoding and decoding networks, in which the sandglass bottleneck design is adopted to alleviate the information a loss in high dimension. In the decoding network, a different sized dilated convolution kernel is used for convolution operation to make the extracted banana stalk features denser. The proposed network is verified by experiments. In the experiments, the detection precision, segmentation accuracy, number of parameters, operation efficiency, and average execution time are used as evaluation metrics, and the proposed network is compared with Resnet_Segnet, Mobilenet_Segnet, and a few other networks. The experimental results show that compared to other networks, the number of network parameters of the proposed network is significantly reduced, the running frame rate is improved, and the average execution time is shortened.

scholarly journals Image Semantic Segmentation Use Multiple-Threshold Probabilistic R-CNN with Feature Fusion

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 207
Author(s):  
Jianxin Liu ◽  
Yushui Geng ◽  
Jing Zhao ◽  
Kang Zhang ◽  
Wenxiao Li
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Feature Fusion ◽  
Probability Model ◽  
Semantic Segmentation ◽  
Segmentation Accuracy ◽  
Experimental Effect ◽  
Multiple Threshold ◽  
Feature Pyramid ◽  
Image Edges

With continuous developments in deep learning, image semantic segmentation technology has also undergone great advancements and been widely used in many fields with higher segmentation accuracy. This paper proposes an image semantic segmentation algorithm based on a deep neural network. Based on the Mask Scoring R-CNN, this algorithm uses a symmetrical feature pyramid network and adds a multiple-threshold architecture to improve the sample screening precision. We employ a probability model to optimize the mask branch of the model further to improve the algorithm accuracy for the segmentation of image edges. In addition, we adjust the loss function so that the experimental effect can be optimized. The experiments reveal that the algorithm improves the results.

Feature Fusion with Deep Neural Network in Kernelized Correlation Filters Tracker

2021 ◽  
Author(s):  
Devira Anggi Maharani ◽  
Carmadi Machbub ◽  
Pranoto Hidaya Rusmin ◽  
Lenni Yulianti
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Feature Fusion ◽  
Correlation Filters ◽  
Kernelized Correlation Filters

scholarly journals A Deep Learning Model for Fault Diagnosis with a Deep Neural Network and Feature Fusion on Multi-Channel Sensory Signals

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4300 ◽  
Author(s):  
Qing Ye ◽  
Shaohu Liu ◽  
Changhua Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Feature Fusion ◽  
Support Vector ◽  
Diagnostic Model ◽  
Sensory Signals ◽  
Sensory Signal ◽  
Deep Architecture ◽  
Final Drive

Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of auto-encoders is adopted to adaptively learn representative features from sensory signal and approximate non-linear relation between symptoms and fault modes. Then, Locality Preserving Projection (LPP) is utilized in the fusion of features extracted from multi-channel sensory signals. Finally, a novel diagnostic model based on multiple DNNs (MDNNs) and softmax is constructed with the input of fused deep features. The proposed method is verified in intelligent failure recognition for automobile final drive to evaluate its performance. A set of contrastive analyses of several intelligent models based on the Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM) and the proposed deep architecture with single sensory signal and multi-channel sensory signals is implemented. The proposed deep architecture of feature extraction and feature fusion on multi-channel sensory signals can effectively recognize the fault patterns of final drive with the best diagnostic accuracy of 95.84%. The results confirm that the proposed method is more robust and effective than other comparative methods in the contrastive experiments.

scholarly journals Text-Independent Speaker Identification Through Feature Fusion and Deep Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 32187-32202 ◽  
Author(s):  
Rashid Jahangir ◽  
Ying Wah TEh ◽  
Nisar Ahmed Memon ◽  
Ghulam Mujtaba ◽  
Mahdi Zareei ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Feature Fusion ◽  
Speaker Identification

scholarly journals Binocular intelligent following robot based on YOLO-LITE

2021 ◽  
Vol 336 ◽  
pp. 03002
Author(s):  
Yuanyuan Zheng ◽  
Jun Ge
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Deep Neural Network ◽  
Frame Rate ◽  
Raspberry Pi ◽  
Detection Accuracy ◽  
Location Error ◽  
Embedded Devices ◽  
Robot System ◽  
Time Performance

In order to solve the problem that the deep neural network model is large in scale, the calculation time is too long, and the real-time performance is severely limited when combined with embedded devices, so studied the intelligent follower robot system based on YOLO-LITE algorithm combined with Raspberry Pi 3B+. The system mainly includes camera processing, target detection and other modules. Obtained the internal and external parameters of the camera through calibration, and according to these parameters to correct the binocular camera. Recognized and located the target in each frame of image, calculated the distance from the camera to the target and the center location error, and driven the car to move. The experimental results show that the following car has excellent real-time performance, the average detection frame rate can reach 20Fps, and the average detection accuracy can reach more than 80%.

scholarly journals Pedestrian and cyclist detection based on deep neural network fast R-CNN

2019 ◽  
Vol 16 (2) ◽  
pp. 172988141982965 ◽  
Author(s):  
Kelong Wang ◽  
Wei Zhou
Keyword(s):  
Neural Network ◽  
Target Detection ◽  
Deep Neural Network ◽  
Detection Method ◽  
Feature Fusion ◽  
Intelligent Vehicles ◽  
Urban Traffic ◽  
Joint Detection ◽  
Difficult Case ◽  
Important Basis

In this article, a unified joint detection framework for pedestrian and cyclist is established to realize the joint detection of pedestrian and cyclist targets. Based on the target detection of fast regional convolution neural network, a deep neural network model suitable for pedestrian and cyclist detection is established. Experiments for poor detection results for small-sized targets and complex and changeable background environment; various network improvement schemes such as difficult case extraction, multilayer feature fusion, and multitarget candidate region input were designed to improve detection and to solve the problems of frequent false detections and missed detections in pedestrian and cyclist target detection. Results of experimental verification of the pedestrian and cyclist database established in Beijing’s urban traffic environment showed that the proposed joint detection method for pedestrians and cyclists can realize the stable tracking of joint detection and clearly distinguish different target categories. Therefore, an important basis for the behavior decision of intelligent vehicles is provided.

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