Xiong Yang

Ming Li, Xiong Yang, Chaofan Wu et al.

Omnidirectional depth estimation enables efficient 3D perception over a full 360-degree range. However, in real-world applications such as autonomous driving and robotics, achieving real-time performance and robust cross-scene generalization remains a significant challenge for existing algorithms. In this paper, we propose a real-time omnidirectional depth estimation method for edge computing platforms named Rt-OmniMVS, which introduces the Combined Spherical Sweeping method and implements the lightweight network structure to achieve real-time performance on edge computing platforms. To achieve high accuracy, robustness, and generalization in real-world environments, we introduce a teacher-student learning strategy. We leverage the high-precision stereo matching method as the teacher model to predict pseudo labels for unlabeled real-world data, and utilize data and model augmentation techniques for training to enhance performance of the student model Rt-OmniMVS. We also propose HexaMODE, an omnidirectional depth sensing system based on multi-view fisheye cameras and edge computation device. A large-scale hybrid dataset contains both unlabeled real-world data and synthetic data is collected for model training. Experiments on public datasets demonstrate that proposed method achieves results comparable to state-of-the-art approaches while consuming significantly less resource. The proposed system and algorithm also demonstrate high accuracy in various complex real-world scenarios, both indoors and outdoors, achieving an inference speed of 15 frames per second on edge computing platforms.

Quan Chen, Xiong Yang, Bolun Zheng et al.

Salient object detection (SOD) in complex environments remains a challenging research topic. Most existing methods perform well in natural scenes with negligible noise, and tend to leverage multi-modal information (e.g., depth and infrared) to enhance accuracy. However, few studies are concerned with the damage of weather noise on SOD performance due to the lack of dataset with pixel-wise annotations. To bridge this gap, this paper introduces a novel Weather-eXtended Salient Object Detection (WXSOD) dataset. It consists of 14,945 RGB images with diverse weather noise, along with the corresponding ground truth annotations and weather labels. To verify algorithm generalization, WXSOD contains two test sets, i.e., a synthesized test set and a real test set. The former is generated by adding weather noise to clean images, while the latter contains real-world weather noise. Based on WXSOD, we propose an efficient baseline, termed Weather-aware Feature Aggregation Network (WFANet), which adopts a fully supervised two-branch architecture. Specifically, the weather prediction branch mines weather-related deep features, while the saliency detection branch fuses semantic features extracted from the backbone with weather features for SOD. Comprehensive comparisons against 17 SOD methods shows that our WFANet achieves superior performance on WXSOD. The code and benchmark results will be made publicly available at https://github.com/C-water/WXSOD

Shangqing Shi, Luoxiao Zhang, Yuchen Yin et al.

Metaheuristics are widely applied for their ability to provide more efficient solutions. The RIME algorithm is a recently proposed physical-based metaheuristic algorithm with certain advantages. However, it suffers from rapid loss of population diversity during optimization and is prone to fall into local optima, leading to unbalanced exploitation and exploration. To address the shortcomings of RIME, this paper proposes a modified RIME with covariance learning and diversity enhancement (MRIME-CD). The algorithm applies three strategies to improve the optimization capability. First, a covariance learning strategy is introduced in the soft-rime search stage to increase the population diversity and balance the over-exploitation ability of RIME through the bootstrapping effect of dominant populations. Second, in order to moderate the tendency of RIME population to approach the optimal individual in the early search stage, an average bootstrapping strategy is introduced into the hard-rime puncture mechanism, which guides the population search through the weighted position of the dominant populations, thus enhancing the global search ability of RIME in the early stage. Finally, a new stagnation indicator is proposed, and a stochastic covariance learning strategy is used to update the stagnant individuals in the population when the algorithm gets stagnant, thus enhancing the ability to jump out of the local optimal solution. The proposed MRIME-CD algorithm is subjected to a series of validations on the CEC2017 test set, the CEC2022 test set, and the experimental results are analyzed using the Friedman test, the Wilcoxon rank sum test, and the Kruskal Wallis test. The results show that MRIME-CD can effectively improve the performance of basic RIME and has obvious superiorities in terms of solution accuracy, convergence speed and stability.

Baoqi Zhao, Xiong Yang, Hoileong Lee et al.

The educational competition optimizer is a recently introduced metaheuristic algorithm inspired by human behavior, originating from the dynamics of educational competition within society. Nonetheless, ECO faces constraints due to an imbalance between exploitation and exploration, rendering it susceptible to local optima and demonstrating restricted effectiveness in addressing complex optimization problems. To address these limitations, this study presents an enhanced educational competition optimizer (IECO-MCO) utilizing multi-covariance learning operators. In IECO, three distinct covariance learning operators are introduced to improve the performance of ECO. Each operator effectively balances exploitation and exploration while preventing premature convergence of the population. The effectiveness of IECO is assessed through benchmark functions derived from the CEC 2017 and CEC 2022 test suites, and its performance is compared with various basic and improved algorithms across different categories. The results demonstrate that IECO-MCO surpasses the basic ECO and other competing algorithms in convergence speed, stability, and the capability to avoid local optima. Furthermore, statistical analyses, including the Friedman test, Kruskal-Wallis test, and Wilcoxon rank-sum test, are conducted to validate the superiority of IECO-MCO over the compared algorithms. Compared with the basic algorithm (improved algorithm), IECO-MCO achieved an average ranking of 2.213 (2.488) on the CE2017 and CEC2022 test suites. Additionally, the practical applicability of the proposed IECO-MCO algorithm is verified by solving constrained optimization problems. The experimental outcomes demonstrate the superior performance of IECO-MCO in tackling intricate optimization problems, underscoring its robustness and practical effectiveness in real-world scenarios.