TL;DR
Comparisons with mainstream state-of-the-art (SOTA) algorithms confirm that the proposed SDM-RTDETR method strikes a competitive trade-off between detection accuracy and model inference efficiency, demonstrating substantial engineering applicability in complex autonomous driving environments.
This work proposes LSO-YOLO, a novel real-time detector optimized for UAV-based small-object detection that significantly reduces parameter count and computational cost by approximately 67% and 18.18%, respectively, while maintaining a high inference speed of 126 FPS on an NVIDIA RTX 3060 GPU, meeting the requirements...
Autonomous vehicles depend on fast and reliable perception systems to detect surrounding vehicles, pedestrians, cyclists, traffic signs, and other road objects in real time. This paper presents a comprehensive survey and analysis of one-stage object detectors for autonomous driving rather than an implementation of a ne...
This work proposes a cascade optimization framework that systematically enhances feature representation and refines multimodal fusion, and introduces the Multi-Scale Contextual Fusion Module (MSCF) to reduce alignment bias.
The overall detection performance of EPLS‐YOLO outperforms that of mainstream object detection models such as RT‐DETR, YOLOv8, YOLOv10 and YOLOv12, which can meet the demand for accurate perception of full‐scale objects in autonomous driving.
A resilient cross-task modeling architecture to overcome the precision-versus-latency dilemma in comprehensive traffic scene understanding, the model's empirical results indicate it surpasses YOLOP as well as HybridNets in detecting objects, segmenting drivable zones, and delineating lane markings.
LCA-Net is presented, a computationally efficient framework for small object detection that balances accuracy with model complexity that demonstrates a favorable accuracy–efficiency trade-off for real-time traffic perception.
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