Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks. This article offers an intriguing architecture for semantic, instance, and panoptic segmentation using EfficientNet-B7 and Bidirectional Feature Pyramid Networks (Bi-FPN). When implemented in place of the EfficientNet-B5 backbone, EfficientNet-B7 strengthens the model’s feature extraction capabilities and is far more appropriate for real-world applications. By ensuring superior multi-scale feature fusion, Bi-FPN integration enhances the segmentation of complex objects across various urban environments. The design suggested is examined on rigorous datasets, encompassing Cityscapes, Common Objects in Context, KITTI Karlsruhe Institute of Technology and Toyota Technological Institute, and Indian Driving Dataset, which replicate numerous real-world driving conditions. During extensive training, validation, and testing, the model showcases major gains in segmentation accuracy and surpasses state-of-the-art performance in semantic, instance, and panoptic segmentation tasks. Outperforming present methods, the recommended approach generates noteworthy gains in Panoptic Quality: +0.4% on Cityscapes, +0.2% on COCO, +1.7% on KITTI, and +0.4% on IDD. These changes show just how efficient it is in various driving circumstances and datasets. This study emphasizes the potential of EfficientNet-B7 and Bi-FPN to provide dependable, high-precision segmentation in computer vision applications, primarily autonomous driving. The research results suggest that this framework efficiently tackles the constraints of practical situations while delivering a robust solution for high-performance tasks involving segmentation.

Non-negative Matrix Factorization (NMF) has been an ideal tool for machine learning. Non-negative Matrix Tri-Factorization (NMTF) is a generalization of NMF that incorporates a third non-negative factorization matrix, and has shown impressive clustering performance by imposing simultaneous orthogonality constraints on both sample and feature spaces. However, the performance of NMTF dramatically degrades if the data are contaminated with noises and outliers. Furthermore, the high-order geometric information is rarely considered. In this paper, a Robust NMTF with Dual Hyper-graph regularization (namely RDHNMTF) is introduced. Firstly, to enhance the robustness of NMTF, an improvement is made by utilizing the l_2,1-norm to evaluate the reconstruction error. Secondly, a dual hyper-graph is established to uncover the higher-order inherent information within sample space and feature spaces for clustering. Furthermore, an alternating iteration algorithm is devised, and its convergence is thoroughly analyzed. Additionally, computational complexity is analyzed among comparison algorithms. The effectiveness of RDHNMTF is verified by benchmarking against ten cutting-edge algorithms across seven datasets corrupted with four types of noise.