Comparison of Faster RCNN and RetinaNet for Car Recognition in Adverse Weather

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Jamshidi Yaser; Okhovat Raziyeh Sadat

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Journal Paper

Paper Information

Journal: NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR Year:2024 | Volume:22 | Issue:1 Page(s): 53-47

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Information Journal Paper

Title

Comparison of Faster RCNN and RetinaNet for Car Recognition in Adverse Weather

Author(s)

Jamshidi Yaser | Okhovat Raziyeh Sadat | Issue Writer Certificate

Keywords

Object recognition

vehicle detection

deep learning

intelligent transportation

image processing in adverse weather

Abstract

vehicle detection and tracking plays an important role in self-driving cars and smart transportation systems. Adverse weather conditions, such as the heavy snow, fog, rain, dust, create dangerous limitations by reducing camera visibility and affect the performance of detection algorithms used in traffic management systems and autonomous cars. In this article, Faster RCNN deep Object recognition network with ResNet50 core and RetinaNet network is used and the accuracy of these two networks for vehicle recognition in adverse weather is investigated. The used dataset is the DAWN file, which contains real-world images collected with different types of adverse weather conditions. The obtained results show that the presented method has increased the detection accuracy from 0. 2% to 75% in the best case, and the highest increase in accuracy is related to rainy conditions.

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