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International Journal of Advances in Engineering & Scientific Research

ENHANCING OBJECT DETECTION IN SELF-DRIVING VEHICLES USING ATROUS SPATIAL PYRAMID POOLING AND TRANSFER LEARNING

Vineeth Reddy

Vineeth Reddy

Machine Learning Engineer

DOI : https://doi.org/10.53882/IJAESR.2024.1102002 Page No : 16-29

Published Online : 2024-12-10

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Self-driving cars depend on object detection to identify and respond to their surroundings in real time. This study investigates how combining transfer learning methods with Atrous Spatial Pyramid Pooling (ASPP) might improve autonomous cars' object detecting ability. In dynamic driving conditions, recognizing objects at different distances and sizes requires the model to be able to collect multi-scale information, which ASPP enhances. By transferring information from current datasets to new, domain-specific tasks, transfer learning uses pre-trained models to speed up training and increase accuracy. The suggested solution addresses major issues such occlusions, fluctuating illumination conditions, and complicated urban environments by combining these two techniques to improve detection accuracy, resilience, and real-time processing capabilities. Experimental results demonstrate significant improvements in object detection performance, validating the effectiveness of ASPP and transfer learning in advancing autonomous vehicle technology. *Keywords:* Object Detection, Self-Driving Vehicles, Atrous Spatial Pyramid Pooling, Transfer Learning, Autonomous Vehicles.

REFERENCES

Arnold E, Dianati M, et. al (2022). Cooperative perception for 3d object detection in driving scenarios using infrastructure sensors. IEEE Trans Intell Transp Syst 23(3):1852–1864. Retrieved From: https://krmzyc-filecloud.oss-cn-beijing.aliyuncs.com/theory/Cooperative%20Perception%20for%203D%20Object%20Detection%20in.pdf
Laxminarayana Korada, et. al (2024). Artificial Intelligence on the Administration Of Financial Markets. DOI: https://doi.org/10.62441/nano-ntp.vi.2493
Pravallika, A., Hashmi, et. al (2024). Deep Learning Frontiers in 3D Object Detection: A Comprehensive Review for Autonomous Driving. IEEE Access. DOI: https://doi.org/10.1109/ACCESS.2024.3456893
Qiao, D., & Zulkernine, F. (2021). Drivable area detection using deep learning models for autonomous driving. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 5233-5238). IEEE. DOI: http://dx.doi.org/10.1109/BigData52589.2021.9671392
Rekha, P., Saranya, T., et. al (2017). Smart Agro Using Arduino and GSM. International Journal of Emerging Technologies in Engineering Research (IJETER) Volume, 5. Retrieved From: https://www.ijeter.everscience.org/Manuscripts/Volume-5/Issue-3/Vol-5-issue-3-M-09.pdf
Ren S, Chen S, Zhang W (2022) Collaborative perception for autonomous driving: current status and future trend. arXiv. DOI: http://dx.doi.org/10.48550/arXiv.2208.10371
Ruixin Ma (2023). Multi-modal information fusion for LiDAR-based 3D object detection framework. DOI: http://dx.doi.org/10.1007/s11042-023-15452-4
Singh, S. K., Choudhary, et. al (2022). Comparative Analysis of Machine Learning Models and Data Analytics Techniques for Fraud Detection in Banking System. International Journal of Core Engineering & Management, 7(1), 64. ISSN 2348-9510. Retrieved From: https://www.researchgate.net/publication/384678305_COMPARATIVE_ANALYSIS_OF_MACHINE_LEARNING_MODELS_AND_DATA_ANALYTICS_TECHNIQUES_FOR_FRAUD_DETECTION_IN_BANKING_SYSTEM
Sujithra, M., Velvadivu, P. et. al (2022). A Study on Psychological Stress of Working Women in Educational Institution Using Machine Learning. In 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE. DOI: https://doi.org/10.1109/ICCCNT54827.2022.9984460
Suresh, K., Reddy, P. et. al (2019). A novel key exchange algorithm for security in internet of things. Indones. J. Electr. Eng. Comput. Sci, 16(3), 1515-1520. DOI: http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1515-1520
Wang, S., Gan, Y., Li, et. al (2024). Vehicle-infrastructure cooperative 3D target detection based on Feature Prediction Atrous Spatial Pyramid Pooling Net. Multimedia Tools and Applications, 1-16. DOI: http://dx.doi.org/10.1007/s11042-024-19807-3
Yang Z, Sun Y, Liu S, et. al (2019) STD: Sparse-to-dense 3D object detector for point cloud. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South): IEEE, pp 1951–1960. Retrieved From: https://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_STD_Sparse-to-Dense_3D_Object_Detector_for_Point_Cloud_ICCV_2019_paper.pdf

Reddy V. (2024), “Enhancing Object Detection In Self-Driving Vehicles Using Atrous Spatial Pyramid Pooling And Transfer Learning”, International Journal of Advances in Engineering & Scientific Research,Volume 11, Issue 2, 2024, pp 16-29. DOI : https://doi.org/10.53882/IJAESR.2024.1102002

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REFERENCES

Arnold E, Dianati M, et. al (2022). Cooperative perception for 3d object detection in driving scenarios using infrastructure sensors. IEEE Trans Intell Transp Syst 23(3):1852–1864. Retrieved From: https://krmzyc-filecloud.oss-cn-beijing.aliyuncs.com/theory/Cooperative%20Perception%20for%203D%20Object%20Detection%20in.pdf
Laxminarayana Korada, et. al (2024). Artificial Intelligence on the Administration Of Financial Markets. DOI: https://doi.org/10.62441/nano-ntp.vi.2493
Pravallika, A., Hashmi, et. al (2024). Deep Learning Frontiers in 3D Object Detection: A Comprehensive Review for Autonomous Driving. IEEE Access. DOI: https://doi.org/10.1109/ACCESS.2024.3456893
Qiao, D., & Zulkernine, F. (2021). Drivable area detection using deep learning models for autonomous driving. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 5233-5238). IEEE. DOI: http://dx.doi.org/10.1109/BigData52589.2021.9671392
Rekha, P., Saranya, T., et. al (2017). Smart Agro Using Arduino and GSM. International Journal of Emerging Technologies in Engineering Research (IJETER) Volume, 5. Retrieved From: https://www.ijeter.everscience.org/Manuscripts/Volume-5/Issue-3/Vol-5-issue-3-M-09.pdf
Ren S, Chen S, Zhang W (2022) Collaborative perception for autonomous driving: current status and future trend. arXiv. DOI: http://dx.doi.org/10.48550/arXiv.2208.10371
Ruixin Ma (2023). Multi-modal information fusion for LiDAR-based 3D object detection framework. DOI: http://dx.doi.org/10.1007/s11042-023-15452-4
Singh, S. K., Choudhary, et. al (2022). Comparative Analysis of Machine Learning Models and Data Analytics Techniques for Fraud Detection in Banking System. International Journal of Core Engineering & Management, 7(1), 64. ISSN 2348-9510. Retrieved From: https://www.researchgate.net/publication/384678305_COMPARATIVE_ANALYSIS_OF_MACHINE_LEARNING_MODELS_AND_DATA_ANALYTICS_TECHNIQUES_FOR_FRAUD_DETECTION_IN_BANKING_SYSTEM
Sujithra, M., Velvadivu, P. et. al (2022). A Study on Psychological Stress of Working Women in Educational Institution Using Machine Learning. In 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE. DOI: https://doi.org/10.1109/ICCCNT54827.2022.9984460
Suresh, K., Reddy, P. et. al (2019). A novel key exchange algorithm for security in internet of things. Indones. J. Electr. Eng. Comput. Sci, 16(3), 1515-1520. DOI: http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1515-1520
Wang, S., Gan, Y., Li, et. al (2024). Vehicle-infrastructure cooperative 3D target detection based on Feature Prediction Atrous Spatial Pyramid Pooling Net. Multimedia Tools and Applications, 1-16. DOI: http://dx.doi.org/10.1007/s11042-024-19807-3
Yang Z, Sun Y, Liu S, et. al (2019) STD: Sparse-to-dense 3D object detector for point cloud. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South): IEEE, pp 1951–1960. Retrieved From: https://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_STD_Sparse-to-Dense_3D_Object_Detector_for_Point_Cloud_ICCV_2019_paper.pdf

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