Original scientific paper
https://doi.org/10.1080/00051144.2024.2314928
Video frame feeding approach for validating the performance of an object detection model in real-world conditions
Keerthi Jayan
; Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
*
B. Muruganantham
; Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
* Corresponding author.
Abstract
The challenge of evaluating deep learning-based object detection models in complex traffic scenarios, characterized by changing weather and lighting conditions, is addressed in this study.
Real-world testing proves time and cost-intensive, leading to the proposal of a Video Frame Feeding (VFF) approach as a solution. The proposed Video Frame Feeding approach acts as a bridge
between object detection models and simulated environments, enabling the generation of realistic scenarios. Leveraging the CarMaker (CM) tool to simulate realistic scenarios, the framework
utilizes a virtual camera to capture the simulated environment and feed video frames to an object
identification model. The VFF algorithm, with automated validation using simulated ground
truth data, enhances detection accuracy to over 95% at 30 frames per second within 130 meters.
Employing the You Only Look Once (YOLO) version 4 and the German Traffic Sign Recognition
Benchmark dataset, the study assesses a traffic signboard identification model across various
climatic conditions. Notably, the VFF algorithm improves accuracy by 2% to 5% in challenging
scenarios like foggy days and nights. This innovative approach not only identifies object detection issues efficiently but also offers a versatile solution applicable to any object detection model,
promising improved dataset quality and robustness for enhanced model performance.
Keywords
Traffic sign detection; YOLOv4 CSPDarkNet53; Video Frame Feeding (VFF); CarMaker simulations; Climatic variations
Hrčak ID:
323052
URI
Publication date:
13.2.2024.
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