CORTEXA
← Browse
crossrefElectronics2024-05-02Cited by 12

Enhancing the Safety of Autonomous Vehicles in Adverse Weather by Deep Learning-Based Object Detection

Biwei Zhang, Murat Simsek, Michel Kulhandjian, Burak Kantarci

Recognizing and categorizing items in weather-adverse environments poses significant challenges for autonomous vehicles. To improve the robustness of object-detection systems, this paper introduces an innovative approach for detecting objects at different levels by leveraging sensors and deep learning-based solutions within a traffic circle. The suggested approach improves the effectiveness of single-stage object detectors, aiming to advance the performance in perceiving autonomous racing environments and minimizing instances of false detection and low recognition rates. The improved framework is based on the one-stage object-detection model, incorporating multiple lightweight backbones. Additionally, attention mechanisms are integrated to refine the object-detection process further. Our proposed model demonstrates superior performance compared to the state-of-the-art method on the DAWN dataset, achieving a mean average precision (mAP) of 99.1%, surpassing the previous result of 84.7%.

View free PDFSource page

Related papers

crossrefElectronics2024-06-30

Empowering Digital Resilience: Machine Learning-Based Policing Models for Cyber-Attack Detection in Wi-Fi Networks

Suryadi MT, Achmad Eriza Aminanto, Muhamad Erza Aminanto

In the wake of the COVID-19 pandemic, there has been a significant digital transformation. The widespread use of wireless communication in IoT has posed security challenges due to its vulnerability to cybercrime. The Indonesian National Police’s Directorate of Cyber Crime is expe…

View free PDFSource page
crossrefElectronics2024-05-24Cited by 6

Research on a Personalized Decision Control Algorithm for Autonomous Vehicles Based on the Reinforcement Learning from Human Feedback Strategy

Ning Li, Pengzhan Chen

To address the shortcomings of previous autonomous decision models, which often overlook the personalized features of users, this paper proposes a personalized decision control algorithm for autonomous vehicles based on RLHF (reinforcement learning from human feedback). The algor…

View free PDFSource page
crossrefElectronics2026-07-24

Deep Learning-Based Defect Segmentation in PAUT B-Scan Images for Nondestructive Evaluation of Metallic Blocks

Le Khuong Phan, Dinh Tuan Nguyen, Thi Thu Ha Vu, Tan Hung Vo, Anh Kiet Nguyen, Jaeyeop Choi, et al.

Metallic blocks and components are indispensable across the aerospace, energy, and heavy-engineering industries, where undetected internal flaws such as cracks, voids, and inclusions may precipitate catastrophic structural failure. Reliable nondestructive evaluation (NDE) is esse…

View free PDFSource page
crossrefElectronics2024-06-21Cited by 77

Autonomous UAV Navigation with Adaptive Control Based on Deep Reinforcement Learning

Yongfeng Yin, Zhetao Wang, Lili Zheng, Qingran Su, Yang Guo

Unmanned aerial vehicle (UAV) navigation plays a crucial role in its ability to perform autonomous missions in complex environments. Most of the existing reinforcement learning methods to solve the UAV navigation problem fix the flight altitude and velocity, which largely reduces…

View free PDFSource page
crossrefElectronics2024-11-06Cited by 14

Machine Learning and Deep Learning Applications in Disinformation Detection: A Bibliometric Assessment

Andra Sandu, Liviu-Adrian Cotfas, Camelia Delcea, Corina Ioanăș, Margareta-Stela Florescu, Mihai Orzan

Fake news is one of the biggest challenging issues in today’s technological world and has a huge impact on the population’s decision-making and way of thinking. Disinformation can be classified as a subdivision of fake news, the main purpose of which is to manipulate and generate…

View free PDFSource page
crossrefElectronics2025-07-07Cited by 2

Research on Autonomous Vehicle Lane-Keeping and Navigation System Based on Deep Reinforcement Learning: From Simulation to Real-World Application

Chia-Hsin Cheng, Hsiang-Hao Lin, Yu-Yong Luo

In recent years, with the rapid development of science and technology and the substantial improvement of computing power, various deep learning research topics have been promoted. However, existing autonomous driving technologies still face significant challenges in achieving rob…

View free PDFSource page