Autonomous Path Finder and Object Detection using an Intelligent Edge Detection Approach
|International Journal of Electrical and Electronics Engineering|
|© 2022 by SSRG - IJEEE Journal|
|Volume 9 Issue 8|
|Year of Publication : 2022|
|Authors : Manasa R, K Karibasappa, Rajeshwari J|
How to Cite?
Manasa R, K Karibasappa, Rajeshwari J, "Autonomous Path Finder and Object Detection using an Intelligent Edge Detection Approach," SSRG International Journal of Electrical and Electronics Engineering, vol. 9, no. 8, pp. 1-7, 2022. Crossref, https://doi.org/10.14445/23488379/IJEEE-V9I8P101
This work carried an approach to automatic detection and recognition of road boundary line demarcation as well as detection of other objects available over the road. Maintaining the vehicles over the road and in proper lanes is important for the autonomous vehicle and driver assistance system. The proposed work has applied an intelligent approach to edge detection using the feed-forward architecture of neural networks, which carried the self-adaptive strategy of transfer function slope in their active nodes. The proposed model has used the neural network as a low pass filter, which tries to develop the same outputs as available inputs. Other information from the image, except edges, was generated by the neural network very well. In contrast, the edges carry high-frequency information, and the neural network doesn't develop the output as it is. Hence a complement of the generated output image to the input image delivered the edges in the input image. The recognition of road boundary was detected as two parallel straight or curve lines. Detecting any object in the lane or over the road was done in two phases. In the first phase, the cover region was divided into several parts, and pixel density was estimated in the near region. The statistical information of pixels' distribution in the near and front region is estimated. In the second stage, a support vector machine-based classifier was used to define the object's presence. Such recognition can help control the vehicle's speed to run the vehicle safely. The proposed work has been applied to various real road images, and obtained results were appreciable. This work can be considered one step further toward developing autonomous vehicles cost-effectively.
Autonomous vehicles, Edge detection, Object detection, Lane detection, and Neural network.
 R. Chen, Et Al, "Learning Lightweight Pedestrian Detector with Hierarchical Knowledge Distillation," IEEE Icip, 2019.
 Automated Vehicles for Safety, Nhtsa Report, 2021.
 V. Kukkala, Et Al, "Advanced Driver Assistance Systems: A Path Toward Autonomous Vehicles ", IEEE Consumer Electronics, Sept 2018
 Yang, X., Yan, J., Wang, W. Et Al, “Brain-Inspired Models for Visual Object Recognition: An Overview,” Artif Intell Rev, 2022. Https://Doi.Org/10.1007/S10462-021-10130-Z
 Jin, M., Yu, L., Zhou, K. Et Al, “Occlusion Tolerant Object Recognition Using Visual Memory Selection Model,” Appl Intell, 2022. Https://Doi.Org/10.1007/S10489-022-03253-5
 Kietzmann, T.C., Lange, S. & Riedmiller, M, “Computational Object Recognition: A Biologically Motivated Approach,” Biol Cybern, vol. 100, no.59, 2009.Https://Doi.Org/10.1007/S00422-008-0281-6
 Bansal, M., Kumar, M., Kumar, M. Et Al, “An Efficient Technique for Object Recognition Using Shi-Tomasi Corner Detection Algorithm,” Soft Comput, vol.25, pp.4423–4432, 2021. Https://Doi.Org/10.1007/S00500-020-05453-Y
 Takagi, R., Horisaki, R. & Tanida, J, ”Object Recognition Through a Multi-Mode Fiber,” Opt Rev, vol. 24, pp.117–120, 2017. Https://Doi.Org/10.1007/S10043-017-0303-5
 Fp., Liu, Je. & Bai, L, “Object Recognition Algorithm Based on Optimized Nonlinear Activation Function-Global Convolutional Neural Network,” Vis Comput, vol. 38, pp.541–553, 2022. Https://Doi.Org/10.1007/S00371-020-02033-X
 Wang, L., Wang, Z., Qiao, Y. Et Al, “ Transferring Deep Object and Scene Representations for Event Recognition In Still Images,” Int J Comput Vis, vol. 126, pp.390–409, 2018.Https://Doi.Org/10.1007/S11263-017-1043-5
 Lu, T., Peng, L. & Zhang, Y, “Edge Feature Based Approach for Object Recognition. Pattern Recognit,” Image Anal, vol.26, pp.350–353, 2016. Https://Doi.Org/10.1134/S1054661816020243
 O'connor, T., Markman, A. & Javidi, B, “Overview of Three-Dimensional Integral Imaging-Based Object Recognition In Low Illumination Conditions with Visible Range Image Sensors,” Sn Appl. Sci, vol.2, no.1724, 2020. Https://Doi.Org/10.1007/S42452-020-03521-4
 Youngseok Lee, Yeppeun Lee, Changhyun Jeong, "A Study on the Correlation Between Driving Behavior and Driver's Take-Over Time of Level 3 Automated Vehicle on Real Roads," International Journal of Engineering Trends and Technology, vol.69, no.2, pp.112-117, 2021, Crossref, https://doi.org/10.14445/22315381/IJETT-V69I2P216
 Jalal, A. Ahmed, A. A. Rafique and K. Kim, "Scene Semantic Recognition Based on Modified Fuzzy C-Mean and Maximum Entropy Using Object-To-Object Relations," In IEEE Access, vol. 9, pp. 27758-27772, 2021, Doi: 10.1109/Access.2021.3058986.
 S. Matteoli, G. Corsini, M. Diani, G. Cecchi and G. Toci, "Automated Underwater Object Recognition By Means of Fluorescence Lidar," In IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 1, pp. 375-393, Jan. 2015, Doi: 10.1109/Tgrs.2014.2322676.
 Yeon Taek Oh, "Study of Wall Climbing Robot Structure and Driving Torque Analysis," International Journal of Engineering Trends and Technology, vol.69, no.9, pp.251-256, 2021, Crossref, https://doi.org/10.14445/22315381/IJETT-V69I9P230.
 E. U. Samani, X. Yang and A. G. Banerjee, "Visual Object Recognition In Indoor Environments Using Topologically Persistent Features," In IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7509-7516, 2021, Doi: 10.1109/Lra.2021.3099460.
 P. Mahalakshmi, R. Vallikannu, "New Miniature Dual Mode Rat-Race Coupler Design for Autonomous Vehicles," International Journal of Engineering Trends and Technology, vol.70, no.1, pp.212-215, 2022.
 Y. Xi Et Al., "See Clearly In the Distance: Representation Learning Gan for Low Resolution Object Recognition," In IEEE Access, vol.8, pp.53203-53214, 2020, Doi: 10.1109/Access.2020.2978980.
 Q. Huang, Z. Cai and T. Lan, "A Single Neural Network for Mixed Style License Plate Detection and Recognition," In IEEE Access, vol. 9, pp. 21777-21785, 2021, Doi: 10.1109/Access.2021.3055243.
 M.Karthikeyan, S.Sathiamoorthy, "Deep Reinforcement Learning for Computerized Steering Angle Control of Pollution-Free Autonomous Vehicle," International Journal of Engineering Trends and Technology, vol.69, no.4, pp.204-208, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I4P228.
 D. -P. Tran, G. -N. Nguyen and V. -D. Hoang, "Hyperparameter Optimization for Improving Recognition Efficiency of An Adaptive Learning System," In IEEE Access, vol. 8, pp. 160569-160580, 2020, Doi: 10.1109/Access.2020.3020930.
 N. Wang, Et Al. Nas-Fcos: "Efficient Search for Object Detection Architectures,” Int J Comput Vis, vol.129, pp.3299–3312, 2021.
 X. Zhang Et Al., "Skynet: A Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems", Proceedings of Machine Learning and Systems, vol.2, pp.216-229, 2019.
 Y. Zhu Et Al, "Acceleration of Pedestrian Detection Algorithm on Novel C2rtl Hw/Sw Co-Design Platform," IEEE Icgcs, 2010.
 Y. Ma Et Al, "Algorithm-Hardware Co-Design of Single Shot Detector for Fast Object Detection on Fpgas, " IEEE/Acm Iccad, 2018
 Shanthi, T., Selvakumar, C., & Prabha, S. U, “Design of Fuzzy Logic Controller for Speed Control of Dc Motor Fed From Solar Pv System,” SSRG International Journal of Electrical and Electronics Engineering, vol.4, no.3, pp.5-9.
 Nair, Remya Ravi, and Ramaprasad Poojary, "Image Denoising Using Decision Tree Based Method," SSRG International Journal of Electronics and Communication Engineering (SSRGIJECE), vol.3, no.6.
 Hemalatha, P., Lakshmi, C. H., & Jilani, S. A. K. Real Time Image Processing Based Robotic Arm Control Standalone System Using Raspberry Pi,” SSRG International Journal of Electronics and Communication Engineering (SSRG-IJECE), vol.2, no.8, 2015