scholarly journals Advanced thermal camera based system for object detection on rail tracks

2018 ◽  
Vol 22 (Suppl. 5) ◽  
pp. 1551-1561 ◽  
Author(s):  
Milan Pavlovic ◽  
Ivan Ciric ◽  
Danijela Ristic-Durrant ◽  
Vlastimir Nikolic ◽  
Milos Simonovic ◽  
...  
Keyword(s):  
Object Detection ◽  
Distance Estimation ◽  
Region Of Interest ◽  
Maximum Error ◽  
Detection Methods ◽  
Thermal Camera ◽  
Image Processing Algorithm ◽  
Detection Of Objects ◽  
Canny Edge ◽  
Reliability And Robustness

In this paper, an advanced thermal camera-based system for detection of objects on rail tracks is presented. Developed system is powered by advanced image processing algorithm, in order to achieve greater reliability and robustness, and tested on set of infrared images captured at night conditions. The goal of this system is to detect objects on rail tracks and next to them and estimate distances between camera stand and detected objects. For that purpose, different edge detection methods are tested, and finally Canny edge detector is selected for rail track detection and for determination of region of interest, further used for analysis in object detection process. In determined region of interest, region-based segmentation is used for object detection. For estimation of distances between camera stand and detected objects, homography based method is used. Validation of estimated distances is done, in respect to real measured distances from camera stand to objects (humans) involved in experiment. Distances are estimated with a maximum error of 2%. System can provide reliable object detection, as well as distance estimation, and for improved robustness and adaptability, artificial intelligence tools can be used.

scholarly journals Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image

2020 ◽  
Vol 10 (13) ◽  
pp. 4646
Author(s):  
Hyun-Cheol Park ◽  
Sang-Woong Lee ◽  
Heon Jeong
Keyword(s):  
Image Processing ◽  
Region Of Interest ◽  
Effective Area ◽  
Thermal Image ◽  
Control Area ◽  
Vertical Position ◽  
Thermal Camera ◽  
Image Processing Algorithm ◽  
Pv Modules ◽  
Thermal Cameras

Recently, there have been many types of research applying drones with a thermal camera to detect deteriorations in photovoltaic (PV) modules. A thermal camera can measure temperatures on the surface of PV modules and find the deteriorated area. However, a thermal camera generally has a lower resolution than a visible camera because of the limitations of cost. Due to different resolutions between the visible and thermal cameras, there are often invalid frames from a thermal camera. In this paper, we describe a gimbal controller with a real-time image processing algorithm to control the angle of the camera to position the region of interest (ROI) in the center of target PV modules to solve this problem. We derived the horizontal angle and vertical position of ROI in visible images using image processing algorithms such as the Hough transform. These values are converted into a PID control signal for controlling the gimbal. This process makes the thermal camera capture the effective area of target PV modules. Finally, experimental results showed that the photovoltaic module’s control area was properly located at the center of the thermal image.

Non-iterative object detection methods in electrical tomography for robotic applications

2017 ◽  
Vol 36 (5) ◽  
pp. 1411-1420 ◽  
Author(s):  
Stephan Mühlbacher-Karrer ◽  
Juliana Padilha Leitzke ◽  
Lisa-Marie Faller ◽  
Hubert Zangl
Keyword(s):  
Object Detection ◽  
Region Of Interest ◽  
Threshold Value ◽  
Detection Methods ◽  
Planar Geometry ◽  
Electrical Tomography ◽  
Content Type ◽  
Speed Up ◽  
Capacitance Tomography ◽  
Robotic Applications

Purpose This paper aims to investigate the usability of the non-iterative monotonicity approach for electrical capacitance tomography (ECT)-based object detection. This is of particular importance with respect to object detection in robotic applications. Design/methodology/approach With respect to the detection problem, the authors propose a precomputed threshold value for the exclusion test to speed up the algorithm. Furthermore, they show that the use of an inhomogeneous split-up strategy of the region of interest (ROI) improves the performance of the object detection. Findings The proposed split-up strategy enables to use the monotonicity approach for robotic applications, where the spatial placement of the electrodes is constrained to a planar geometry. Additionally, owing to the improvements in the exclusion tests, the selection of subregions in the ROI allows for avoiding self-detection. Furthermore, the computational costs of the algorithm are reduced owing to the use of a predefined threshold, while the detection capabilities are not significantly influenced. Originality/value The presented simulation results show that the adapted split-up strategies for the ROI improve significantly the detection performance in comparison to the traditional ROI split-up strategy. Thus, the monotonicity approach becomes applicable for ECT-based object detection for applications, where only a reduced number of electrodes with constrained spatial placement can be used, such as in robotics.

scholarly journals Weighted Ensemble Object Detection with Optimized Coefficients for Remote Sensing Images

2020 ◽  
Vol 9 (6) ◽  
pp. 370
Author(s):  
Atakan Körez ◽  
Necaattin Barışçı ◽  
Aydın Çetin ◽  
Uçman Ergün
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Object Detection ◽  
Mean Average Precision ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Average Precision ◽  
Proposed Model ◽  
Detection Of Objects ◽  
Very High

The detection of objects in very high-resolution (VHR) remote sensing images has become increasingly popular with the enhancement of remote sensing technologies. High-resolution images from aircrafts or satellites contain highly detailed and mixed backgrounds that decrease the success of object detection in remote sensing images. In this study, a model that performs weighted ensemble object detection using optimized coefficients is proposed. This model uses the outputs of three different object detection models trained on the same dataset. The model’s structure takes two or more object detection methods as its input and provides an output with an optimized coefficient-weighted ensemble. The Northwestern Polytechnical University Very High Resolution 10 (NWPU-VHR10) and Remote Sensing Object Detection (RSOD) datasets were used to measure the object detection success of the proposed model. Our experiments reveal that the proposed model improved the Mean Average Precision (mAP) performance by 0.78%–16.5% compared to stand-alone models and presents better mean average precision than other state-of-the-art methods (3.55% higher on the NWPU-VHR-10 dataset and 1.49% higher when using the RSOD dataset).

scholarly journals IMPLEMENTASI METODE DETEKSI TEPI CANNY PADA OBJEK SEBAGAI MODEL KEAMANAN APLIKASI PADA SMARTPHONE ANDROID

Petir ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 16-20
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Edge Detection ◽  
Error Rate ◽  
Detection Methods ◽  
Canny Edge Detection ◽  
Minimum Error ◽  
Detection Of Objects ◽  
The Face ◽  
Canny Edge ◽  
Data Objects ◽  
Shape And Size

The development of technology push security system applications on android smartphone to develop one of its features that is detection of the object. The detection of Objects is a technology that allows us to identify or verify an object through a digital image by matching the texture of the object with the curve of the data objects stored in the database. For example, to match the curve of the face such as the nose, eyes and chin. There are several methods to support the work of object detection among which edge detection. Edge detection can represent the objects contained in the image of the shape and size as well as information about the texture of an object. the best method of edge detection is canny edge detection which has the minimum error rate compared with other edge detection methods. Canny edge detection will generate the image that has been processed into a new image. The new image will be stored on a database that will be matched to the image of a new object that is used as the opening applications on android smartphone.

scholarly journals Segmentation Method for Face Modelling in Thermal Images

2020 ◽  
Vol 3 (2) ◽  
pp. 99
Author(s):  
Albar Albar ◽  
Hendrick Hendrick ◽  
Rahmad Hidayat
Keyword(s):  
Object Detection ◽  
Detection Methods ◽  
Segmentation Method ◽  
Thermal Camera ◽  
Final Model ◽  
Face Model ◽  
Thermal Images ◽  
Face Area ◽  
The Face ◽  
Rgb Images

Face detection is mostly applied in RGB images. The object detection usually applied the Deep Learning method for model creation. One method face spoofing is by using a thermal camera. The famous object detection methods are Yolo, Fast RCNN, Faster RCNN, SSD, and Mask RCNN. We proposed a segmentation Mask RCNN method to create a face model from thermal images. This model was able to locate the face area in images. The dataset was established using 1600 images. The images were created from direct capturing and collecting from the online dataset. The Mask RCNN was configured to train with 5 epochs and 131 iterations. The final model predicted and located the face correctly using the test image.

Detection and Segmentation of Medical Images Using Generic Algorithms

2021 ◽  
Vol 3 (1) ◽  
pp. 39-46
Author(s):  
Hardev Mukeshbhai Khandhar ◽  
Chintan M. Bhatt ◽  
Simon Fong
Keyword(s):  
Medical Imaging ◽  
Medical Image ◽  
Medical Images ◽  
Region Of Interest ◽  
Primary Objective ◽  
Detection Methods ◽  
Modern World ◽  
Diagnosis And Prognosis ◽  
Canny Edge ◽  
Image Segementation

Image processing plays an indispensable and significant role in the development of various fields like medical imaging, astronomy, GIS, disaster management, agriculture monitoring, and so on. Medical images which are recorded in digital forms are processed by high-end computers to extract whatever information we desire. In the fast-developing modern world of medical imaging diagnosis and prognosis, where manual photo interpretation is time-consuming, automatic object detection from devices like CT-Scans and MRIs has limited potential to generate the required results. This article addresses the process of identifying Region of Interests in cancer based medical images based on combination of Otsu’s algorithm and Canny edge detection methods. The primary objective of this paper is to derive meaningful and potential information from medical image in different scenarios by applying the image segementation in combination with genetic algorithms in a robust manner to detect region of interest.

Saliency detection in deep learning era: trends of development

2019 ◽  
pp. 10-36 ◽  
Author(s):  
M. N. Favorskaya ◽  
L. C. Jain
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Event Detection ◽  
Visual Analysis ◽  
Saliency Detection ◽  
Salient Object Detection ◽  
Public Image ◽  
Detection Methods ◽  
Salient Object ◽  
Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

scholarly journals Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility

2020 ◽  
Vol 18 (1) ◽  
pp. 91
Author(s):  
Louis Lecrosnier ◽  
Redouane Khemmar ◽  
Nicolas Ragot ◽  
Benoit Decoux ◽  
Romain Rossi ◽  
...  
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Indoor Environment ◽  
Distance Estimation ◽  
Depth Estimation ◽  
Detection Algorithm ◽  
Use Case ◽  
Detection Distance ◽  
Detection Depth ◽  
Sort Algorithm

This paper deals with the development of an Advanced Driver Assistance System (ADAS) for a smart electric wheelchair in order to improve the autonomy of disabled people. Our use case, built from a formal clinical study, is based on the detection, depth estimation, localization and tracking of objects in wheelchair’s indoor environment, namely: door and door handles. The aim of this work is to provide a perception layer to the wheelchair, enabling this way the detection of these keypoints in its immediate surrounding, and constructing of a short lifespan semantic map. Firstly, we present an adaptation of the YOLOv3 object detection algorithm to our use case. Then, we present our depth estimation approach using an Intel RealSense camera. Finally, as a third and last step of our approach, we present our 3D object tracking approach based on the SORT algorithm. In order to validate all the developments, we have carried out different experiments in a controlled indoor environment. Detection, distance estimation and object tracking are experimented using our own dataset, which includes doors and door handles.

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