Vehicle Detection Network using CNN and instance Segmentation
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Abstract
These days, when an accident happens, people either hide from the emergency services or cause a lot of disturbance while reporting it, or the accident goes unreported and by the time help arrives, it’s too late. A comprehensive system has been developed to actively detect all types of traffic accidents and notify the appropriate parties. In the event of an accident, this includes the closest police station, hospitals, general ambulances, the registered owner of the vehicle involved in the accident, and their emergency contacts. In the event of a hit-and-run, the police can obtain the vehicle number of the accused vehicle. Numerous applications exist for vehicle detection in aerial pictures, and most vehicle detection techniques employ the bounding-box approach for localization. Using a private dataset, a convolutional neural network is proposed in this letter to detect cars. Based on experimental data, CNN performs better, with an accuracy rate of 94.54%.
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Articles
References
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International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1), 2023, pp. 1–6.
[4]. X. Li, G. Tian, Z. Lu, and G. Zhang, “Yolo v5-max: A multi-object detection algorithm in complex scenes,” in 2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS), 2023, pp. 1–6.
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[9]. C. Bae, K. Kang, G. Liu, and Y. Y. Chung, “A novel real time video tracking framework using adaptive discrete swarm optimization,” Expert Systems with Applications, vol. 64, pp. 385–399, 2016.
[10]. A. Ojha, S. P. Sahu, and D. Dewangan, “Vehicle detection through instance segmentation using mask r-cnn for intelligent vehicle system,” 05 2021, pp. 954–959.
[11]. F. Li, Z. He, and Y. Yu, “Vehicle target detection in day-night mode based on yolov5,” in 2023 IEEE International Conference on Image Processing and Computer Applications (ICIPCA), 2023, pp. 641–646.
[12]. A. Panthakkan, N. Valappil, S. Al-Mansoori, and H. Al-Ahmad, “Ai based automatic vehicle detection from unmanned aerial vehicles (uav) using yolov5 model,” in 2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS), vol. Five, 2022, pp. 1–5.
[13]. S. Zhang and Y. Tong, “Nighttime vehicle detection algorithm enhanced by nightvisiongan,” in 2023 4th International Conference on Computer Vision, Image and Deep Learning (CVIDL), 2023, pp. 672–676.
[14]. J. Shen, W. Zhou, N. Liu, H. Sun, D. Li, and Y. Zhang, “An anchor free lightweight deep convolutional network for vehicle detection in aerial images,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 24 330–24 342, 2022.
[15]. M. Todevska, A. Zlatkova, D. Taskovski, and Z. Ivanovski, “Real–time vehicle detection based on wavelet decomposition and cnn,” in 2023 30th International Conference on Systems, Signals and Image Processing (IWSSIP), 2023, pp. 1–5.
[16]. J. Shen, N. Liu, H. Sun, D. Li, and Y. Zhang, “Lightweight deep network with context information and attention mechanism for vehicle detection in aerial image,” IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2022.
[17]. J. M, R. P, A. Kunjumon, M. Thamban, and A. Roy, “Convolutional neural networks (cnn)-based vehicle crash detection and alert system,” in 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), 2023, pp. 161–164.
[18]. Q. Du, T. Celik, Q. Wang, and H.-C. Li, “Fully convolutional lightweight pyramid network for vehicle detection in aerial images,” IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2022.
[19]. P. B. Mishra, A. Malik, M. Safa, G. Saranaya, and D. Arun, “Enhanced object detection with deep convolutional neural networks for vehicle detection,” in 2022 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), 2022, pp. 1–7.
[20]. V. Agarwal, “Research on data preprocessing and categorization technique for smartphone review analysis,” International Journal of Computer Applications, vol. 131, no. 4, pp. 30–36, 2015.
[21]. L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al Shamma, J. Santamar´ıa, M. A. Fadhel, M. Al-Amidie, and L. Farhan, “Review of deep learning: Concepts, cnn architectures, challenges, applications, future directions,” Journal of big Data, vol. 8, pp. 1–74, 2021.
[22]. A. Pathak and A. Dhole,“Lungs cancer identification by deep learning 3d cnn architecture.” Grenze International Journal of Engineering & Technology (GIJET), vol. 8, no. 1, 2022.