Persistent Fish School-Inspired Deep Belief Network for Object Detection in Varied Weather Traffic Surveillance
Persistent Fish School-Inspired Deep Belief Network for Object Detection in Varied Weather Traffic Surveillance |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-6 |
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| Year of Publication : 2024 | ||
| Author : V. Valarmathi, S. Dhanalakshmi |
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| DOI : 10.14445/22315381/IJETT-V72I6P119 | ||
How to Cite?
V. Valarmathi, S. Dhanalakshmi, "Persistent Fish School-Inspired Deep Belief Network for Object Detection in Varied Weather Traffic Surveillance," International Journal of Engineering Trends and Technology, vol. 72, no. 6, pp. 178-194, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I6P119
Abstract
Traffic surveillance is pivotal in ensuring public safety and efficient urban mobility. With the continuous improvements in computer vision, surveillance systems can now identify things automatically in real-time, greatly expanding their possibilities. However, the challenges associated with object detection, particularly in diverse weather conditions, pose a considerable obstacle. Adverse weather elements, such as rain and snow, can impede the accuracy of detection algorithms, impacting the overall effectiveness of traffic surveillance systems. This research addresses these challenges by introducing the Persistent Fish School Search-Inspired Deep Belief Network (PFSS-DBN), a novel algorithm designed to bolster object detection in varying weather climates. Inspired by fish schools’ persistent and adaptive nature, PFSS-DBN leverages deep belief networks to navigate complex visual data. The algorithm dynamically adapts its parameters, optimizing its performance for weather scenarios. This adaptability enhances detection accuracy and ensures reliable surveillance outcomes even in challenging conditions. The study employs the AAU RainSnow Traffic Surveillance Dataset to evaluate the proposed PFSS-DBN algorithm. Through comprehensive experimentation, the results demonstrate the superior performance of PFSS-DBN compared to traditional methods, showcasing its efficacy in mitigating the impact of adverse weather on object detection. The findings underscore the potential of PFSS-DBN as a valuable solution for improving the reliability of traffic surveillance systems, particularly in regions prone to diverse weather conditions
Keywords
Adaptive parameter optimization, Nature-Inspired computing, Object detection, PFSS-DBN, Traffic surveillance, Weather-Adaptive algorithms.
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