A Visual Area Detection Algorithm based on Mirror Splicing for Motorcycle Indirect Vision Test

Abstract

Abstract Motorcycle indirect vision devices are important safety components, and the visual area is required to meet industry standards. There is a need for effective algorithms to detect and analyze the relevant visual areas within these mirrors to enhance motorcycle safety. However, it is a challenge to test rearview mirror vision in unstructured environments given variations in illumination, occlusion, and object scale. We propose a visual area detection algorithm based on mirror stitching for a motorcycle indirect vision test. First, an edge-based VGG16-Unet (EBV16-Unet) network is employed to extract binocular mirror information and eliminate the complex background. Second, gradient-based topology-preserving image stitching and multi-band hybrid Laplacian pyramid-based image blending algorithms are utilized to complete binocular mirror information acquisition. Finally, a sequential detection method for adaptive marker color and shape features is used to establish the visual area. The EBV16-Unet algorithm achieved an accuracy of 98.63% for precision, 98.71% for recall, 98.58% for F1, and 98.37% for mean intersection-over-union (MIOU), surpassing the comparative models of PSPNet, DeepLab v3+, and HRNet and exhibited superior generalization ability. The binocular vision splicing effect experiment revealed a horizontal splicing error of 0.114322 ± 0.0674 and vertical splicing error of 0.124287 ± 0.063302, calculated using a standard checkerboard. The rearview mirror vision test operation experiment results confirm that the Motorcycle Indirect Vision Test System (MIVTS) offers convenience, simplicity and high accuracy. MIVTS successfully accomplishes the unstructured motorcycle rearview mirror vision test, thereby establishing an advanced theoretical foundation for computer vision-based automated vehicle inspection.