scholarly journals Corrigendum to “Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques”

2021 ◽  
Vol 2021 ◽  
pp. 1-1
Author(s):  
Qimei Wang ◽  
Feng Qi ◽  
Minghe Sun ◽  
Jianhua Qu ◽  
Jie Xue
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Detection Techniques ◽  
Tomato Disease

scholarly journals Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Qimei Wang ◽  
Feng Qi ◽  
Minghe Sun ◽  
Jianhua Qu ◽  
Jie Xue
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Detection Methods ◽  
Disease Detection ◽  
Deep Convolutional Neural Networks ◽  
Detection Techniques ◽  
Tomato Diseases ◽  
Validation Set ◽  
Tomato Disease

This study develops tomato disease detection methods based on deep convolutional neural networks and object detection models. Two different models, Faster R-CNN and Mask R-CNN, are used in these methods, where Faster R-CNN is used to identify the types of tomato diseases and Mask R-CNN is used to detect and segment the locations and shapes of the infected areas. To select the model that best fits the tomato disease detection task, four different deep convolutional neural networks are combined with the two object detection models. Data are collected from the Internet and the dataset is divided into a training set, a validation set, and a test set used in the experiments. The experimental results show that the proposed models can accurately and quickly identify the eleven tomato disease types and segment the locations and shapes of the infected areas.

scholarly journals Object detection in real time based on improved single shot multi-box detector algorithm

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Ashwani Kumar ◽  
Zuopeng Justin Zhang ◽  
Hongbo Lyu
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Single Layer ◽  
Single Shot ◽  
Convolutional Network ◽  
Detection Techniques ◽  
Computational Performance ◽  
Speed Up

Abstract In today’s scenario, the fastest algorithm which uses a single layer of convolutional network to detect the objects from the image is single shot multi-box detector (SSD) algorithm. This paper studies object detection techniques to detect objects in real time on any device running the proposed model in any environment. In this paper, we have increased the classification accuracy of detecting objects by improving the SSD algorithm while keeping the speed constant. These improvements have been done in their convolutional layers, by using depth-wise separable convolution along with spatial separable convolutions generally called multilayer convolutional neural networks. The proposed method uses these multilayer convolutional neural networks to develop a system model which consists of multilayers to classify the given objects into any of the defined classes. The schemes then use multiple images and detect the objects from these images, labeling them with their respective class label. To speed up the computational performance, the proposed algorithm is applied along with the multilayer convolutional neural network which uses a larger number of default boxes and results in more accurate detection. The accuracy in detecting the objects is checked by different parameters such as loss function, frames per second (FPS), mean average precision (mAP), and aspect ratio. Experimental results confirm that our proposed improved SSD algorithm has high accuracy.

FOREIGN OBJECT DETECTION IN TV AND LWIR IMAGERY OF A RUNWAY BY DEEP CONVOLUTIONAL NEURAL NETWORKS

2017 ◽  
Vol 9 (4) ◽  
pp. 89-96
Author(s):  
V.V. Kniaz ◽  
V.V. Fedorenko ◽  
V.A. Mizginov ◽  
V.A. Knyaz ◽  
W. Purgathofer
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Foreign Object ◽  
Deep Convolutional Neural Networks

Rich feature hierarchies for accurate object detection and semantic segmentation

2021 ◽  
pp. 115-126
Author(s):  
A.Y. Virasova ◽  
D.I. Klimov ◽  
O.E. Khromov ◽  
I.R. Gubaidullin ◽  
V.V. Oreshko
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Hardware Implementation ◽  
Training Data ◽  
Fine Tuning ◽  
Preliminary Training ◽  
Detection Techniques ◽  
Foreign Production ◽  
Element Base

Formulation of the problem. Over the past few years, there has been little progress in object detection techniques. The most efficient are complex computational ensemble methods, which usually combine several low-level image properties with high-level properties. However, every day interest in artificial intelligence is growing, and the idea of using neural networks on board a spacecraft, with the possibility of making decisions and issuing one-time commands, is very promising, since it makes it possible to analyze a large data stream in real time without resorting to ground station, thereby not losing information when transmitting a packet. The purpose of the work is to conduct research on the possibility of effective use of modern models of neural networks, to develop a methodology for their use in the problem of object detection and analysis of the element base for hardware implementation with the possibility of using convolutional neural networks for thermovideotelemetry on board a spacecraft. Results of work. An approach has been formulated that combines two key ideas: 1) application of high-throughput convolutional neural networks for downward processing of image regions in order to localize and segment objects; 2) preliminary training for the auxiliary task, followed by fine tuning of the domain, which gives a significant increase in performance in the case when the training data is insufficient. The analysis of the element base for the possibility of hardware implementation of neural networks on board a spacecraft using electrical radio products of domestic and foreign production is carried out. Practical significance. The efficiency of preliminary network training for an auxiliary task is shown, followed by fine tuning of the subject area. A technique is described that makes it possible to increase the average accuracy of detecting objects in an image by more than 30%. The analysis of the existing element base, the possibility of hardware implementation of neural networks on board the spacecraft using electrical radio products of domestic and foreign production, as well as the criteria for selecting key elements.

An energy-efficient deep convolutional neural networks coprocessor for multi-object detection

2020 ◽  
Vol 98 ◽  
pp. 104737
Author(s):  
Yuancong Wu ◽  
J.J. Wang ◽  
Kun Qian ◽  
Yanchen Liu ◽  
Rui Guo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Energy Efficient ◽  
Deep Convolutional Neural Networks

scholarly journals Evaluation and Evolution of Object Detection Techniques YOLO and R-CNN

2019 ◽  
Vol 8 (2S3) ◽  
pp. 824-829 ◽  
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Problem Solving ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Mean Average Precision ◽  
Average Precision ◽  
Trade Off ◽  
Detection Techniques

Object detection has boomed in areas like image processing in accordance with the unparalleled development of CNN (Convolutional Neural Networks) over the last decade. The CNN family which includes R-CNN has advanced to much faster versions like Fast-RCNN which have mean average precision(Map) of up to 76.4 but their frames per second(fps) still remain between 5 to 18 and that is comparatively moderate to problem-solving time. Therefore, there is an urgent need to increase speed in the advancements of object detection. In accordance with the broad initiation of CNN and its features, this paper discusses YOLO (You only look once), a strong representative of CNN which comes up with an entirely different method of interpreting the task of detecting the objects. YOLO has attained fast speeds with fps of 155 and map of about 78.6, thereby surpassing the performances of other CNN versions appreciably. Furthermore, in comparison with the latest advancements, YOLOv2 attains an outstanding trade-off between accuracy and speed and also as a detector possessing powerful generalization capabilities of representing an entire image

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