ArticleScientific reports2025
Optimization of deep learning-based faster R-CNN network for vehicle detection.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Performance trade-offs between dense prediction and sparse query mechanisms for brain tumor MRI detection: a comparative study of YOLOv8 and RT-DETR.Frontiers in medicine · 2026Article
- Applications, image analysis, and interpretation of computer vision in medical imaging.Frontiers in radiology · 2025Review
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Authors and funding
2 authors.
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Abstract
Optimizing hyperparameters in object detection models is critical for enhancing performance, particularly in domain-specific tasks such as vehicle detection. This research systematically investigates the optimization of key hyperparameters for the Faster R-CNN model to maximize its efficiency in detecting vehicles. We evaluated the impact of various base CNN architectures (VGG-16, ResNet-50, Inceptionv3), solvers (sgdm, rmsprop, adam), learning rates (10
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.