Comparative Study of Faster Region-Based Convolutional Neural Networks with Inception V2 and Single Shot Detector with Inception V2 on Their Signature Detection Capabilities

Bounding box algorithms are useful in localization of image patterns. Recently, utilization of convolutional neural networks on X-ray images has proven a promising disease prediction technique. However, pattern localization over prediction has always been a challenging task with inconsistent coordinates, sizes, resolution and capture positions of an image. Several model architectures like Fast R-CNN, Faster R-CNN, Histogram of Oriented Gradients (HOG), You only look once (YOLO), Region-based Convolutional Neural Networks (R-CNN), Region-based Fully Convolutional Networks (R-FCN), Single Shot Detector (SSD), etc. are used for object detection and localization in modern-day computer vision applications. SSD and region-based detectors like Fast R-CNN or Faster R-CNN are very similar in design and implementation, but SSD have shown to work efficiently with larger frames per second (FPS) and lower resolution images. In this article, we present a unique approach of SSD with a VGG-16 network as a backbone for feature detection of bounding box algorithm to predict the location of an anomaly within chest X-ray image.

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

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-020-01826-x ◽

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.

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Designing a Supermarket Service Robot Based on Deep Convolutional Neural Networks

Symmetry ◽

10.3390/sym12030360 ◽

2020 ◽

Vol 12 (3) ◽

pp. 360

Author(s):

Aihua Chen ◽

Benquan Yang ◽

Yueli Cui ◽

Yuefen Chen ◽

Shiqing Zhang ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Labor Cost ◽

Service Robot ◽

Raspberry Pi ◽

Single Shot ◽

Deep Convolutional Neural Networks ◽

Reduce Labor Cost ◽

Robot Performance ◽

Grasping And Placing

In order to save people’s shopping time and reduce labor cost of supermarket operations, this paper proposes to design a supermarket service robot based on deep convolutional neural networks (DCNNs). Firstly, according to the shopping environment and needs of supermarket, the hardware and software structure of supermarket service robot is designed. The robot uses a robot operating system (ROS) middleware on Raspberry PI as a control kernel to implement wireless communication with customers and staff. So as to move flexibly, the omnidirectional wheels symmetrically installed under the robot chassis are adopted for tracking. The robot uses an infrared detection module to detect whether there are commodities in the warehouse or shelves or not, thereby grasping and placing commodities accurately. Secondly, the recently-developed single shot multibox detector (SSD), as a typical DCNN model, is employed to detect and identify objects. Finally, in order to verify robot performance, a supermarket environment is designed to simulate real-world scenario for experiments. Experimental results show that the designed supermarket service robot can automatically complete the procurement and replenishment of commodities well and present promising performance on commodity detection and recognition tasks.

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Helmet Detection Using Faster Region-Based Convolutional Neural Networks and Single-Shot MultiBox Detector

10.1109/icscc51209.2021.9528256 ◽

2021 ◽

Author(s):

Prajval Mohan ◽

Pranav Narayan ◽

Lakshya Sharma ◽

M. Anand

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Single Shot

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Convolutional Neural Networks for Biological Sequence Taxonomic Classification: A Comparative Study

Advances in Intelligent Systems and Computing - Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019 ◽

10.1007/978-3-030-31129-2_48 ◽

2019 ◽

pp. 523-533

Author(s):

Marwah A. Helaly ◽

Sherine Rady ◽

Mostafa M. Aref

Keyword(s):

Neural Networks ◽

Comparative Study ◽

Convolutional Neural Networks ◽

Taxonomic Classification ◽

Biological Sequence

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Single-Shot 3D Shape Reconstruction Using Structured Light and Deep Convolutional Neural Networks

Sensors ◽

10.3390/s20133718 ◽

2020 ◽

Vol 20 (13) ◽

pp. 3718 ◽

Cited By ~ 2

Author(s):

Hieu Nguyen ◽

Yuzeng Wang ◽

Zhaoyang Wang

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

3D Imaging ◽

Structured Light ◽

Depth Map ◽

Shape Reconstruction ◽

Single Shot ◽

Deep Convolutional Neural Networks ◽

3D Shape ◽

3D Shape Reconstruction

Single-shot 3D imaging and shape reconstruction has seen a surge of interest due to the ever-increasing evolution in sensing technologies. In this paper, a robust single-shot 3D shape reconstruction technique integrating the structured light technique with the deep convolutional neural networks (CNNs) is proposed. The input of the technique is a single fringe-pattern image, and the output is the corresponding depth map for 3D shape reconstruction. The essential training and validation datasets with high-quality 3D ground-truth labels are prepared by using a multi-frequency fringe projection profilometry technique. Unlike the conventional 3D shape reconstruction methods which involve complex algorithms and intensive computation to determine phase distributions or pixel disparities as well as depth map, the proposed approach uses an end-to-end network architecture to directly carry out the transformation of a 2D image to its corresponding 3D depth map without extra processing. In the approach, three CNN-based models are adopted for comparison. Furthermore, an accurate structured-light-based 3D imaging dataset used in this paper is made publicly available. Experiments have been conducted to demonstrate the validity and robustness of the proposed technique. It is capable of satisfying various 3D shape reconstruction demands in scientific research and engineering applications.

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