Single-Frame Difference-Based Image Fusion Glare-Resistant Detection System in Green Energy Vehicles

作     者：Lyu, Xiang Wang, Nan Gao, Jia 

作者机构：Xinyang Agr & Forestry Univ Sch Informat Engn Xinyang 464000 Peoples R China SEGi Univ Grad Sch Business GSB Kota Damansara 47810 Petaling Jaya Malaysia 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2024年第12卷

页      面：110977-110991页

核心收录：

基　　金：Scientific and Technological Project of Henan Province ''Tea Leaf Disease Recognition Based on Adaptive Meta-Transfer Learning and Small Samples' 

主　　题：Pedestrians Image processing Night vision Image fusion Accuracy Safety Image color analysis Green energy Detection algorithms Green energy vehicles single-frame difference image fusion glare-free pedestrian detection 

摘      要：Green energy vehicles often use technologies that reduce lower inherent noise. However, adverse weather condition and low visibility at night can cause a glare effect from the headlights of oncoming cars. This poses a major threat to traffic safety. In order to solve this problem, this study initially adopts single frame difference for video frame selection, which reduces the computational load of image processing pipeline. Then, combined with visible and infrared images, this paper uses non-downsampled contourlet transform to achieve glare elimination. Finally, an improved convolutional network is used to detect pedestrians in anti-glare images, and volumetric Kalman filter algorithm is used to track pedestrians. Through these operations, the research establishes a Single-Frame Difference-Based Image Fusion Glare-Resistant Detection System applicable to green energy vehicles. The experimental analysis shows that the designed system can eliminate glare more than 80%, and the pedestrian detection accuracy reaches 95.44%. The constructed system aids green energy vehicles in accurately perceiving their surroundings during nighttime driving, ensuring safe travel.