The recognition of traffic signs plays a crucial role in maintaining road safety and facilitating the smooth flow of traffic. This paper introduces a novel small traffic sign recognition algorithm based YOLOv8m network structure. Initially, a new feature fusion structure, SSF, is proposed to replace the Neck part of YOLOv8, incorporating a lower-scale detection head with minimal cost and better leveraging the correlation of pyramid layers for enhanced multi-scale feature fusion. Following this, the C2f module is augmented with a self-attention operation, AttnConv, to boost the network's proficiency in extracting features from small traffic signs. Finally, the use of MPDIoU effectively reduces computation and accelerates the network's rapid fitting. Experimental validation on the processed TT100K dataset has shown that the improved model achieved a mean Average Precision(mAP) of 90.6%, which is a 2.4% improvement over the original YOLOv8m model and surpasses the current state-of-the-art, effectively proving the superiority of the proposed model in the recognition of small traffic signs.
The academic paper introduces a multi-strategy enhanced Sand Cat Swarm Algorithm (TL-SCSO) designed to tackle the challenges of limited population diversity, poor convergence accuracy, and susceptibility to local optima. The algorithm leverages Cubic chaos mapping to enrich the initial population distribution, ensuring a more diverse and representative starting point. Additionally, an adaptive t-distribution perturbation approach is employed to maintain population diversity throughout the optimization process. The incorporation of the golden ratio into the Levi's flight strategy enables the algorithm to effectively navigate away from local optima, enhancing its overall performance. Finally, the algorithm is utilized to optimize the hyperparameters of BiTCN-BiLSTM-Attention, thereby improving model training speed and prediction accuracy. The algorithm's efficacy is demonstrated through its application to data from a photovoltaic power station in central China, showcasing its superior stability and convergence accuracy under various weather conditions.
Research on underwater target detection is highly valuable in a variety of domains, including resource exploration, marine ecology, and the marine environment. The accuracy of detection is frequently unsatisfied due to blur distortion and dispersion in the underwater environment, particularly for small underwater target objects. To tackle such problems, this paper proposes underwater target detection method, named YOLOv7-ASF. This framework significantly increases the speed and precision of underwater target recognition by combining spatial and scale characteristics. Employing the YOLOv7 framework as the backbone, we first use the Scale Sequence Feature Fusion (SSFF) module to improve the network's multi-scale information extraction capabilities. To provide comprehensive information, we fuse feature maps of various scales by the TPE (Triple Feature Encoder) module. Moreover, to enhance the model's detection performance, the channel and position attention mechanism (CPAM), which focuses on small objects connected to information channels and spatial positions, is added to combine the SSFF and TPE modules. Experiments on the URPC2020 data set demonstrate the effectiveness of the proposed method.
In order to improve the voltage quality of microgrid, and lower the configuration cost of energy storage system, a method based on Improved Grey Wolf Optimizer (IGWO) for the optimal configuration of the energy storage capacity in microgrid is proposed. The IGWO algorithm generates the initial population by introducing Tent chaotic mapping to enhances the diversity of the population; by linearly decreasing inertia weight, better balance local search and global search capabilities; by randomly adjusting the control parameter, the optimization performance of the algorithm is greatly improved. The simulation results verify the superiority of IGWO algorithm in solving the energy storage capacity allocation problem of microgrid, which provides a new solution to improve the operation economy of microgrid system.
Whale optimization algorithm (WOA), as a new artificial intelligence algorithm, has been successfully applied in many fields, but it has many shortcomings in solving reactive power optimization problems. In view of the shortcomings of Whale Optimization Algorithm in dealing with the problem about the optimization of reactive power, such as low convergence accuracy, falling into local optimality easily and converging slowly, this paper improves the basic WOA from four aspects: initial population, inertia weight, convergence factor and spiral update. An Improved Whale Optimization Algorithm (IWOA) is proposed to improve the searching ability and convergence speed of the algorithm. The reactive power Optimization model was established by introducing penalty function to minimize the active power network loss of the system. The simulation was carried out using IEEE-33 nodes, and compared with Whale optimization algorithm, Particle Swarm Optimization (PSO) and Gray Wolf optimization (GWO). The results verify the feasibility and effectiveness of IWOA in addressing the reactive power optimization problem.
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