Aug 2026· Journal of Machine Learning and Information Security· Vol 2· 1 citation· 27 references
TL;DR
A C2f Parnet module is designed based on the Parnet parallel network architecture to reduce network depth and computational latency, and GSConv is modified by employing dual 3 × 3 convolutions to decrease parameter count.
Abstract
This paper proposes enhancements to the YOLOv8s model for autonomous driving object detection tasks. Firstly, a C2f Parnet module is designed based on the Parnet parallel network architecture to reduce network depth and computational latency. Secondly, GSConv is modified by employing dual 3 × 3 convolutions to decrease parameter count. Thirdly, L2-scored structural pruning is introduced to compress redundant channels. Finally, detection accuracy is improved using EIoU loss. Experiments on the KITTI dataset demonstrate that the improved model maintains a mAP50 of 0.917. The performance of this model is almost comparable to the original model (0.939), but with parameters reduced to 11 million and GFLOPs decreased to 26.2. Compared with other methods, L2 pruning achieved the highest mAP50-95 score of 0.753. In complex traffic environments, this model demonstrates robust real-time detection capabilities, can be flexibly deployed, and is a useful choice for lightweight detection in autonomous driving.
For object detection in complex road situations, such as inadequate detection performance and difficulties caused by vehicle occlusion and cluttered environments, this paper pursues a YOLOv11s-based object detection framework. The algorithm successfully designed a novel PEConv module. This module integrates a partial c...
Yu-Lin Chen, Yi-Ning Wang, Jian-Wei-Zhao-Xin Wang et al.· SAE technical paper series· 0 citations
Object detection is a fundamental perception task in intelligent transportation systems and autonomous driving. These systems rely on computer vision techniques to enable intelligent perception in complex traffic environments. However, object detection models still struggle in real-world driving scenarios, particularly...
Tien Sang Ngo, Thanh Van Dinh, H. D. Lê· International Conference on...· 0 citations
Motivated by the reduced detection accuracy of small targets, including traffic lights and traffic signs, in autonomous driving scenes with complex background interference, this work presents an improved YOLOv8-P2-ECA target detection method that integrates a P2 small target detection head with an Efficient Channel Att...
Wen-Bin Cai· International Conference on...· 0 citations
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.
Tao Feng, Siyi Yang, Wenli Wang et al.· Expert Syst. J. Knowl. Eng.· 0 citations
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.
Traffic accidents caused by insufficient driver reaction time continue to be a major concern in road transportation safety. Forward Collision Warning (FCW) systems are widely recognized as an effective solution for reducing collision risks by providing early alerts to drivers. This study presents the development and pe...
Muhamad Ilham Rahman, Ramdhani, Yusep Sukrawan et al.· Jurnal Informatika, Teknolog...· 0 citations
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