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With the growth of deep learning and digital image processing, it is require knowing about the facts of deep learning. Deep learning is major factor of object detection. In existing research R-CNN algorithm used for detect the objects from an image while in this proposed research method we are using YOLO ( you only look once)algorithm to detect the different objects in a single image. This is less time consuming because we only recognize a image once and we detect the whole objects in an image.

Digital image processing, image recognition, Accuracy, time complexity, histogram , K-MEANS,YOLO

[1] Yali Amit and Pedro Felzenszwalb, University of Chicago
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[4] Jussi Jokela “PERSON COUNTER USING REAL-TIME OBJECT DETECTION AND A SMALL NEURAL NETWORK” TURKU UNIVERSITY OF APPLIED SCIENCES
[5] http://cs231n.github.io/convolutional-networks/
[6] ImageNet Classification with Deep Convolutional Neural Networks by Alex Krizhevsky University of Toronto kriz@cs.utoronto.ca Ilya Sutskever University of Toronto ilya@cs.utoronto.ca
[7] Wenqing Chu, Deng Cai “ Deep feature based contextual model for object detection” Neurocomputing 275, 1035–1042, 2018
[8] S. Gidaris , N. Komodakis , Locnet: improving localization accuracy for object de- tection, in: Proceedings of the IEEE Conference on Computer Vision and Pat- tern Recognition, pp. 789–798, 2016.
[9] Gemma Roig_1;2 Xavier Boix_1;2 Horesh Ben Shitrit2 Pascal Fua2 “Conditional Random Fields for Multi-Camera Object Detection”1ETHZ, Zurich (Switzerland) 2EPFL, Lausanne (Switzerland)
[10] Navneet Dalal, Bill Triggs. Histograms of Oriented Gradients for Human Detection. Cordelia Schmid and Stefano Soatto and Carlo Tomasi. International Conference on Computer Vision.
[11] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. In: NIPS, 2015.
[12] Wei Liu1, Dragomir Anguelov2, Dumitru Erhan “SSD: Single Shot MultiBox Detector” UNC Chapel Hill 2Zoox Inc. 3Google Inc. 4University of Michigan, Ann-Arbor 1wliu@cs.unc.edu, 2drago@zoox.com.
[13] Joseph Redmon University of Washington pjreddie@cs.washington.edu Santosh Divvala Allen Institute for Artificial Intelligence” You Only Look Once: Unified, Real-Time Object Detection”
[14] Albert Soto i Serrano” YOLO Object Detector for Onboard Driving Images” , ESCOLA D’ENGINYERIA (EE), UNIVERSITAT AUTONOMA DE BARCELONA (UAB)
[15] Z. Shen and X. Xue. Do more dropouts in pool5 feature maps for better object detection. arXiv preprint arXiv:1409.6911, 2014.
[16] R. B. Girshick. Fast R-CNN. CoRR, abs/1504.08083, 2015.
[17] Geethapriya. S, N. Duraimurugan, S.P. Chokkalingam, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-8, Issue-3S, February 2019.

Amandeep Kaur, Deepinder Kaur, "Yolo Deep Learning Model Based Algorithm for Object Detection," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.174-178, 2020.

Block-Chain is referred as the list of transactions stored in multiple participating servers rather than on a central transaction server. Each and every participant in the Block-Chain network is granted access to an up-to-date copy of this encrypted catalog so they can read, write, and validate transactions. Block-Chains have recently gained a lot of attention in IoT solutions even though they are used mostly in the financial domain. Block-Chain can relatively support in accomplishing the vision of distributed IoT, facilitating transactions and coordination among interacting devices. The two technologies Block-Chain and IoT are used to build end to end automated and secured solutions. Internets of things (IoT) solutions are being effectively implemented in many different sectors, such as healthcare, warehousing, transportation, and logistics. Current unified, Cloud-based IoT solutions may not gauge and meet the network security defies faced by large-scale enterprises. The use of Block-Chain as a distributed catalog of transactions and peer-to-peer communication among contributing nodes can unravel such problems. This paper gives an idea of Block-Chain-enabled IoT solutions and shows how to use the Block-Chain platform for an IoT application in a multi partner environment.

Cloud-based IoT; Network security; Block-Chain Services; Application Programming Interface; Messaging Protocol

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B. Mukunthan, S. Govindaraju, S.K. Komagal Yallini, S. VibinChander, C. RanjithKumar, "The role of Block-Chain in Cloud-based IoT solutions to build end-to-end automated and secured solutions," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.179-186, 2020.

With the growth of computer vision, digital image processing is necessary to provide a clear image to the user. In existing research detection of pole side objects with the help of an LiDar which only detect the object but not with clear transparency in proposed research we are try to give the clear vision of the pole side object with the help of fusion of LiDar and orthophoto and also improve the accuracy of an image.

Digital image processing, image recognition, SVM, Accuracy, image enhancement, Machine learning, Histogram

[1] Li Yan et.al “Detection and classification of pole-like road objects from mobile LiDAR data in motorway environment”
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[4] Deep Learning Approach for Building Detection Using Orthophoto Journal of Sensors Volume 2018, Article ID 7212307.
[5] Bisheng Yang, Zhen Dong, Gang Zhao, Wenxia Dai ierarchical extraction of urban objects from mobile laser scanning data sISPRS Journal of Photogrammetry and Remote Sensing, Volume 99, pp. 45-57, 2015.
[6] Sheng Xu, Shanshan Xu, Ning Ye, Fa Zhu”Automatic extraction of street trees’ nonphotosynthetic components from MLS data”International Journal of Applied Earth Observation and Geoinformation, Volume 69, pp. 64-77, 2018.
[7] Haiyan Guan, Member, IEEE, Yongtao Yu, Jonathan Li, Senior Member, IEEE, and Pengfei Liu” Pole-Like Road Object Detection in Mobile LiDAR Data via Supervoxel and Bag-of-Contextual-Visual-Words Representation”
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[17] https://www.elprocus.com/lidar-light-detection-and-ranging-working-application/

Payal, Rasneet Kaur, "Deep Learning Approach for Pole like Road object Detection Using LiDAR–Orthophoto Fusion," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.187-190, 2020.

This paper focuses on drought forecasting, using Artificial Neural Network (ANN) and predicts the values of drought condition derived using Remote Sensing data of Indore (M.P). We have used the NDVI data as input data of ANN model for drought forecasting, and determine Standard Vegetation Index (SNDVI). Artificial Neural networks operate on the principle of learning from a training set. There is a large variety of neural network models and learning procedures. Two classes of neural networks that are usually used for prediction applications are feed-forward networks and recurrent networks. They often train both of these networks using back-propagation algorithm.

Data Source, Artificial Neural Network.

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Rajesh Kumar Sharma, Mayank Rajput, Rahul Sharma, "Drought Forecasting, using Artificial Neural Network (ANN) and Predict Values of Drought Condition Derived using NDVI Data," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.191-193, 2020.