scholarly journals Developing an On-Road Object Detection System Using Monovision and Radar Fusion

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 116
Author(s):  
Ya-Wen Hsu ◽  
Yi-Horng Lai ◽  
Kai-Quan Zhong ◽  
Tang-Kai Yin ◽  
Jau-Woei Perng
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Spatial Clustering ◽  
Detection System ◽  
Warning System ◽  
Recognition System ◽  
Target Object ◽  
Fusion Algorithm ◽  
Detection Rates ◽  
Distance Information

In this study, a millimeter-wave (MMW) radar and an onboard camera are used to develop a sensor fusion algorithm for a forward collision warning system. This study proposed integrating an MMW radar and camera to compensate for the deficiencies caused by relying on a single sensor and to improve frontal object detection rates. Density-based spatial clustering of applications with noise and particle filter algorithms are used in the radar-based object detection system to remove non-object noise and track the target object. Meanwhile, the two-stage vision recognition system can detect and recognize the objects in front of a vehicle. The detected objects include pedestrians, motorcycles, and cars. The spatial alignment uses a radial basis function neural network to learn the conversion relationship between the distance information of the MMW radar and the coordinate information in the image. Then a neural network is utilized for object matching. The sensor with a higher confidence index is selected as the system output. Finally, three kinds of scenario conditions (daytime, nighttime, and rainy-day) were designed to test the performance of the proposed method. The detection rates and the false alarm rates of proposed system were approximately 90.5% and 0.6%, respectively.

scholarly journals Manipulation Planning for Object Re-Orientation Based on Semantic Segmentation Keypoint Detection

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2280
Author(s):  
Ching-Chang Wong ◽  
Li-Yu Yeh ◽  
Chih-Cheng Liu ◽  
Chi-Yi Tsai ◽  
Hisasuki Aoyama
Keyword(s):  
Neural Network ◽  
Robot Manipulator ◽  
Detection System ◽  
Semantic Segmentation ◽  
Target Object ◽  
Planning System ◽  
Normal Vector ◽  
Manipulation Planning ◽  
Keypoint Detection ◽  
3D Keypoint Detection

In this paper, a manipulation planning method for object re-orientation based on semantic segmentation keypoint detection is proposed for robot manipulator which is able to detect and re-orientate the randomly placed objects to a specified position and pose. There are two main parts: (1) 3D keypoint detection system; and (2) manipulation planning system for object re-orientation. In the 3D keypoint detection system, an RGB-D camera is used to obtain the information of the environment and can generate 3D keypoints of the target object as inputs to represent its corresponding position and pose. This process simplifies the 3D model representation so that the manipulation planning for object re-orientation can be executed in a category-level manner by adding various training data of the object in the training phase. In addition, 3D suction points in both the object’s current and expected poses are also generated as the inputs of the next operation stage. During the next stage, Mask Region-Convolutional Neural Network (Mask R-CNN) algorithm is used for preliminary object detection and object image. The highest confidence index image is selected as the input of the semantic segmentation system in order to classify each pixel in the picture for the corresponding pack unit of the object. In addition, after using a convolutional neural network for semantic segmentation, the Conditional Random Fields (CRFs) method is used to perform several iterations to obtain a more accurate result of object recognition. When the target object is segmented into the pack units of image process, the center position of each pack unit can be obtained. Then, a normal vector of each pack unit’s center points is generated by the depth image information and pose of the object, which can be obtained by connecting the center points of each pack unit. In the manipulation planning system for object re-orientation, the pose of the object and the normal vector of each pack unit are first converted into the working coordinate system of the robot manipulator. Then, according to the current and expected pose of the object, the spherical linear interpolation (Slerp) algorithm is used to generate a series of movements in the workspace for object re-orientation on the robot manipulator. In addition, the pose of the object is adjusted on the z-axis of the object’s geodetic coordinate system based on the image features on the surface of the object, so that the pose of the placed object can approach the desired pose. Finally, a robot manipulator and a vacuum suction cup made by the laboratory are used to verify that the proposed system can indeed complete the planned task of object re-orientation.

scholarly journals Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm

2020 ◽  
Vol 28 (S2) ◽  
Author(s):  
Asmida Ismail ◽  
Siti Anom Ahmad ◽  
Azura Che Soh ◽  
Mohd Khair Hassan ◽  
Hazreen Haizi Harith
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Object Classification ◽  
Detection Algorithm ◽  
Learning Object ◽  
Sliding Windows ◽  
Detection Algorithms

The object detection system is a computer technology related to image processing and computer vision that detects instances of semantic objects of a certain class in digital images and videos. The system consists of two main processes, which are classification and detection. Once an object instance has been classified and detected, it is possible to obtain further information, including recognizes the specific instance, track the object over an image sequence and extract further information about the object and the scene. This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. MiniVGGNet is an architecture network used to train an object classification, and the data used for this purpose was collected from specific indoor environment building. For object detection, sliding windows and image pyramids were used to localize and detect objects at different locations, and non-maxima suppression (NMS) was used to obtain the final bounding box to localize the object location. Based on the experiment result, the percentage of classification accuracy of the network is 80% to 90% and the time for the system to detect the object is less than 15sec/frame. Experimental results show that there are reasonable and efficient to combine classic object detection method with a deep learning classification approach. The performance of this method can work in some specific use cases and effectively solving the problem of the inaccurate classification and detection of typical features.

scholarly journals A Convolutional Neural Network Algorithm for the Optimization of Emergency Nursing Rescue Efficiency for Critical Patients

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Xueyan Chen ◽  
Xiaofei Zhong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Recognition Rate ◽  
Recognition System ◽  
Emergency Nursing ◽  
Network Algorithm ◽  
Average Recognition Rate ◽  
Neural Network Algorithm ◽  
Critical Patients

In order to help pathologists quickly locate the lesion area, improve the diagnostic efficiency, and reduce missed diagnosis, a convolutional neural network algorithm for the optimization of emergency nursing rescue efficiency of critical patients was proposed. Specifically, three convolution layers and convolution kernels of different sizes are used to extract the features of patients’ posture behavior, and the classifier of patients’ posture behavior recognition system is used to learn the feature information by capturing the nonlinear relationship between the features to achieve accurate classification. By testing the accuracy of patient posture behavior feature extraction, the recognition rate of a certain action, and the average recognition rate of all actions in the patient body behavior recognition system, it is proved that the convolution neural network algorithm can greatly improve the efficiency of emergency nursing. The algorithm is applied to the patient posture behavior detection system, so as to realize the identification and monitoring of patients and improve the level of intelligent medical care. Finally, the open source framework platform is used to test the patient behavior detection system. The experimental results show that the larger the test data set is, the higher the accuracy of patient posture behavior feature extraction is, and the average recognition rate of patient posture behavior category is 97.6%, thus verifying the effectiveness and correctness of the system, to prove that the convolutional neural network algorithm has a very large improvement of emergency nursing rescue efficiency.

scholarly journals Brand Detection System using Deep Learning

2020 ◽  
Vol 9 (9) ◽  
pp. 497-500
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Visual Representation ◽  
Cosmetic Products ◽  
Medical Object

In this paper a method of recognizing logos of the brand of cosmetic products using deep learning. There are several of hoax product which easily copies the famous brand’s logo and deteriorates the company’s image. The machine learning has proved to be useful in various of the fields like medical, object detection, vehicle logo recognitions. But till now very few of the works have been performed in cosmetic field. This field is covered using the model sequential convolutional neural network using Tensorflow and Keras. For the visual representation of the result Tensorboard is used. Work have been started with two of the brands-Lakme and L’Oreal. Depending upon the success of this technique, further brands for logo may be added for recognition. The accuracy of approximately 80% was obtained using this technique.

scholarly journals Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3595 ◽  
Author(s):  
Anderson Aparecido dos Santos ◽  
José Marcato Junior ◽  
Márcio Santos Araújo ◽  
David Robledo Di Martini ◽  
Everton Castelão Tetila ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Spatial Resolution ◽  
Tree Species ◽  
Remote Sensing Data ◽  
Target Object ◽  
Detection Methods ◽  
Individual Tree ◽  
Art Object

Detection and classification of tree species from remote sensing data were performed using mainly multispectral and hyperspectral images and Light Detection And Ranging (LiDAR) data. Despite the comparatively lower cost and higher spatial resolution, few studies focused on images captured by Red-Green-Blue (RGB) sensors. Besides, the recent years have witnessed an impressive progress of deep learning methods for object detection. Motivated by this scenario, we proposed and evaluated the usage of Convolutional Neural Network (CNN)-based methods combined with Unmanned Aerial Vehicle (UAV) high spatial resolution RGB imagery for the detection of law protected tree species. Three state-of-the-art object detection methods were evaluated: Faster Region-based Convolutional Neural Network (Faster R-CNN), YOLOv3 and RetinaNet. A dataset was built to assess the selected methods, comprising 392 RBG images captured from August 2018 to February 2019, over a forested urban area in midwest Brazil. The target object is an important tree species threatened by extinction known as Dipteryx alata Vogel (Fabaceae). The experimental analysis delivered average precision around 92% with an associated processing times below 30 miliseconds.

scholarly journals Research on object detection algorithm based on deep learning for mobile Terminal

2021 ◽  
Vol 2083 (3) ◽  
pp. 032019
Author(s):  
Jie Luo ◽  
Jufeng Ye ◽  
Lan Shan ◽  
Qunying He
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Mobile Terminal ◽  
Detection Algorithm ◽  
Target Object ◽  
Raspberry Pi ◽  
Average Precision ◽  
Detection Ability ◽  
Typical Object

Abstract In order to improve object detection ability of robot, this study introduces an object detection algorithm which is based on deep learning. Firstly, a neural network that contains Convolution Layer, Pooling Layer and Fullly Connection Layer is designed to recognize whether the target object in image. Secondly, the Faster-RCNN, as a typical object detection algorithm, is used to detect the position of target object in image. Finally, the algorithm is transplanted on Raspberry Pi for deployment and testing. Results show that this algorithm can work well on Raspberry PI, and the mAP (mean average precision) and FPS (frames per second) reach 97% and 3. Meanwhile, the position of target object is accurately marked in image. Therefore, this study implies that the object detection algorithm based on deep learning can be implemented on mobile terminal and is helpful to improve object detection ability of mobile terminal.

scholarly journals Correction: Ye, T.; et al. Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode. Sensors 2018, 18, 1916

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3044
Author(s):  
Tao Ye ◽  
Baocheng Wang ◽  
Ping Song ◽  
Juan Li
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Feature Fusion ◽  
Detection System ◽  
Railway Traffic

The authors wish to make the following corrections to this paper [...]

scholarly journals Fast and Cost Efficient Face Detection System with CNN Using Raspberry PI

2019 ◽  
Vol 8 (6) ◽  
pp. 3037-3039
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Gabor Filter ◽  
Detection System ◽  
Cloud Service ◽  
Recognition System ◽  
Raspberry Pi ◽  
Face Recognition System ◽  
Cost Efficient

Face detection is an important process when it comes to computer vision. It will serve as an input to a Facial expression and Face recognition system. Modern “C.C.T.V” cameras with face detection features are costly and only few are connected to the internet. This paper proposes a Face detection system which detects faces with a fusion of Convolutional neural network and Gabor Filter. Gabor filter is used to extract important facial features and Convolutional neural network is used to train the model. Model weights files are executed in Raspberry PI which is cost efficient. Raspberry pi is connected to cloud service which will alert the user with SMS and E-mail.

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