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

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.