Abstract
Computer vision methods are conventionally used for identification of object boundaries from an image of interest. Here, it has been performed extensive performance analysis of computer vision techniques for detection of objects present in a room from the acquired real-time lux sensor data. A server-client wireless network is initially set up to capture horizontal lux data of a room using unmanned surface vehicle. The client unit comprises of light intensity sensor and IR transceiver module for capturing the positional lux information of an indoor space. The sensing block is interfaced with a wireless enabled microcontroller and it transmits the sensor values to a sever unit with the help of a wireless router. Now, the obtained values are stored in a computer system working as server module. The acquired real-time positional lux data are converted into image and different computer vision techniques e.g. Canny method, Prewitt method, Sobel method, and Laplacian of Gaussian method have been used to detect the possible position and object shape in the indoor space. Comparative assessment based on performance indices like recall, precision, and F1 value has been carried out to find out the most acceptable solution.
Similar content being viewed by others
References
Y. Omura, H. Masuda, Y. Mimura, Feasibility investigation of obstacle-avoiding sensors unit without image processing. J. Sensors 2013, 1–11 (2013) (article id 643815). https://doi.org/10.1155/2013/643815
Y. Zhu, B. Yi, T. Guo, A simple outdoor environment obstacle detection method based on information fusion of depth and infrared. J. Robotics 2016, 1–10 (2016) (article id 2379685). https://doi.org/10.1155/2016/2379685
G. Csaba, L. Somlyai, Z. Vamossy, in Proceedings of 10th IEEE International Symposium on Applied Machine Intelligence and Informatics. Differences Between Kinect and Structured Lighting Sensor in Robot Navigation. (Slovakia, 2012), pp. 85–90.
H.-H. Pham, T.-L. Le, N. Vuillerme, Real-time obstacle detection system in indoor environment for the visually impaired using Microsoft Kinect sensor. J. Sensors 2016, 1–13 (2016) (article id 3754918). https://doi.org/10.1155/2016/3754918
M.A. Ansari, D. Kurchaniya, M. Dixit, A comprehensive analysis of image edge detection techniques. Int. J. Multim. Ubiq. Eng. 12(11), 1–12 (2017). https://doi.org/10.14257/ijmue.2017.12.11.01
A. Ghosh, P.K. Kundu, G. Sarkar, in Proceedings of IEEE International Conference on Applied Signal Processing. Automated Lux Measurement for Lighting Design in Indoor Space Using Mobile Sensor. (India, 2018), pp. 106–109
A. Ghosh, P.K. Kundu, G. Sarkar, Design and real-time implementation of cloud based indoor illumination monitoring system. J. Inst. Eng. Ser. B 101(3), 223–227 (2020). https://doi.org/10.1007/s40031-020-00448-7
B. Crnokić, S. Rezić, S. Pehar, in Proceedings of 27th Daaam International Symposium on Intelligent Manufacturing and Automation. Comparison of Edge Detection Methods for Obstacles Detection in a Mobile Robot Environment. (Austria, 2016), pp. 0235–0244
W. Chen, S. Chen, H. Guo, X. Ni, Welding flame detection based on color recognition and progressive probabilistic Hough transform. Concurr. Comput. Pract. Exp. 32(19), 1–9 (2020). https://doi.org/10.1002/cpe.5815
L.-H. Gong, C. Tian, W.-P. Zou, N.-R. Zhou, Robust and imperceptible watermarking scheme based on Canny edge detection and SVD in the contourlet domain. Multim. Tools Appl. 80(1), 439–461 (2021). https://doi.org/10.1007/s11042-020-09677-w
T.Y. Erwin, Detection of blood vessels in optic disc with maximum principal curvature and wolf thresholding algorithms for vessel segmentation and Prewitt edge detection and circular Hough transform for optic disc detection. Iranian J. Sci. Technol. Trans. Elect. Eng. 45(2), 435–446 (2021). https://doi.org/10.1007/s40998-020-00367-9
O. Li, P.-l. Shui, Subpixel blob localization and shape estimation by gradient search in parameter space of anisotropic Gaussian kernels. Signal Processing 171(107495), 1–15 (2020) https://doi.org/10.1016/j.sigpro.2020.107495
S. Dhar, M.K. Kundu, Accurate segmentation of complex document image using digital shearlet transform with neutrosophic set as uncertainty handling tool. Appl. Soft Comput. 61, 412–426 (2017). https://doi.org/10.1016/j.asoc.2017.08.005
S. Dhar, M.K. Kundu, Interval type-2 fuzzy set and human vision based multi-scale geometric analysis for text-graphics segmentation. Multim. Tools Appl. 78(16), 22939–22957 (2019). https://doi.org/10.1007/s11042-019-7649-6
A. Ghosh, P.K. Kundu, G. Sarkar, in Proceedings of 2nd IEEE International Conference on Control, Measurement and Instrumentation. Computer Vision Based Obstacle Identification Using Real-Time Illumination Sensor Data. (India, 2021), pp. 190–19.
A. Ghosh, P.K. Kundu, G. Sarkar, Internet of human centric lighting: a brief overview on indian aspects. Sci. Culture J. 86(November–December), 350–356 (2020). https://doi.org/10.36094/sc.v86.2020.Internet_of_Human_Centric.Ghosh.350
A. Ghosh, P. Satvaya, P.K. Kundu, G. Sarkar, Calibration of RGB sensor for estimation of real-time correlated color temperature using machine learning regression techniques. Optik-Int. J. Light Electron Optics 258(168954), 1–5 (2022). https://doi.org/10.1016/j.ijleo.2022.168954
A. Ghosh, P.K. Kundu, G. Sarkar, Similarity Detection of Illuminance Images using Eigenface Method. J. Inst. Eng. Ser. B (2022). https://doi.org/10.1007/s40031-022-00750-6
A. Ghosh, P. Satvaya, P.K. Kundu, G. Sarkar, Machine learning based illuminance estimation from rgb sensor in a wireless network. Wirel. Personal Commun. 1–17. https://doi.org/10.1007/s11277-022-09639-5
A. Ghosh, P.K. Kundu, G. Sarkar, in Proceedings of Springer International Conference on Industrial Instrumentation and Control. Classification of Illuminance Images using Eigenface Technique. (India, 2021), pp. 77–85
Funding
The authors received no funding for this manuscript.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ghosh, A., Kundu, P.K. & Sarkar, G. Object Detection Using Computer Vision Methods on Real-Time Lux Sensor Data. J. Inst. Eng. India Ser. B 103, 1659–1663 (2022). https://doi.org/10.1007/s40031-022-00756-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40031-022-00756-0