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AlexDarkNet: Hybrid CNN architecture for real-time Traffic monitoring with unprecedented reliability

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Abstract

In recent years, the proliferation of vehicles on roadways, accompanied by an escalating demand for augmented safety and refined traffic management, has fueled substantial progress in the domains of computer vision and deep learning methodologies. This technological surge has precipitated ground-breaking applications, with real-time vehicle detection emerging as a linchpin. Capability to accurately detect vehicles in a real-time holds profound implications across diverse domains. In the context of autonomous driving, it underpins essential functions such as path planning, collision avoidance, and decision-making, essential for the safe operation of autonomous vehicles. In the arena of traffic monitoring, it empowers transportation authorities to acquire real-time insights into traffic dynamics, congestion patterns, and incident detection, thereby optimizing traffic flow and resource allocation strategies. Furthermore, within surveillance systems, real-time vehicle detection equips security personnel with the means to swiftly identify potential security threats, enhancing overall situational awareness and safety. This paper contributes to this dynamic landscape by introducing an innovative architecture expressly crafted for vehicle detection. Leveraging state-of-the-art computer vision techniques and deep learning methodologies, this architecture addresses the evolving challenges posed by contemporary traffic scenarios. Through the enhancement of real-time vehicle detection capabilities, our aim is to fortify the foundations of safety, efficiency, and security within vehicular contexts. The paper includes a fusion of AlexNet and Darknet accompanied by a comparative analysis. It is expected to deliver unmatched dependability and accuracy in real-time traffic monitoring, setting a new standard for performance in this application.

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Our research is still in progress and involved complex modeling, simulations that used high-performance computing clusters and proprietary software. Sharing the underlying data and code is not feasible due to technical limitations, including data size and compatibility issues.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, 500075, India

    Rakhi Madhukarrao Joshi & D. S. Rao

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  1. Rakhi Madhukarrao Joshi
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  2. D. S. Rao
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Correspondence to Rakhi Madhukarrao Joshi.

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Joshi, R.M., Rao, D.S. AlexDarkNet: Hybrid CNN architecture for real-time Traffic monitoring with unprecedented reliability. Neural Comput & Applic 36, 7133–7141 (2024). https://doi.org/10.1007/s00521-024-09450-2

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