ArticleScientific reports2025
Dual-stage feature specialization network for robust visual object detection in autonomous vehicles.
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 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Semi-Active Suspension Systems: From Advanced Control Algorithms to Emerging Off-Road and Agricultural Applications.Sensors (Basel, Switzerland) · 2026Review
- DER-YOLO: A Lightweight Stage-Wise Feature Calibration Network for Onboard Real-Time Small-Object Detection.Sensors (Basel, Switzerland) · 2026Article
- Fault Diagnosis Method for Electric Vehicle In-Wheel Motor Bearings Based on Improved SVMD and ResNet-KAN.Sensors (Basel, Switzerland) · 2026Article
- A review: research progress on intelligent technologies for orchard yield monitoring.Frontiers in plant science · 2026Review
- DDRN: DETR with dual refinement networks for autonomous vehicle object detection.Scientific reports · 2025Article
- A SCG-YOLOv8n potato counting framework with efficient mobile deployment.Scientific reports · 2025Article
- Applications, image analysis, and interpretation of computer vision in medical imaging.Frontiers in radiology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Efficient feature representation is critical for accurate visual perception in autonomous vehicles. Existing two-stage object detection methods often suffer from feature interference between candidate region generation and classification regression tasks, leading to suboptimal performance in complex scenes. To address this, we propose a Dual-Stage Feature Specialization Network (DSFSN) that decouples feature extraction: MobileNetV3 is employed for lightweight candidate region generation, while ResNet-FPN enhances multi-scale feature fusion for precise classification. Extensive experiments on PASCAL VOC and MS COCO datasets demonstrate state-of-the-art performance, achieving 81.6% mAP (9.3% higher than Faster R-CNN) and 29.3% AP on MS COCO, with a 14.9% improvement in small object detection. Real-world tests under diverse conditions (e.g., rain, night) validate the robustness of our method for autonomous driving applications. This work provides a novel framework for balancing accuracy and efficiency in visual perception systems.
Indexed as
Identifiers
What OpenQuestion holds
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.