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In urban environments, semantic segmentation using computer vision plays a pivotal role in understanding and interpreting the diverse elements within urban imagery. The Cityscapes dataset, widely used for semantic segmentation in urban scenes, predominantly features urban elements like buildings and vehicles but lacks aquatic elements. Recognizing this limitation, our study introduces a method to enhance the Cityscapes dataset by incorporating aquatic classes, crucial for a comprehensive understanding of coastal urban environments. To achieve this, we employ a dual-model approach using two advanced neural networks. The first network is trained on the standard Cityscapes dataset, while the second focuses on aquatic scenes. We adeptly integrate aquatic features from the marine-focused model into the Cityscapes imagery. This integration is carefully executed to ensure a seamless blend of urban and aquatic elements, thereby creating an enriched dataset that reflects the realities of coastal cities more accurately. Our method is evaluated by comparing the enhanced Cityscapes model with the original on a set of diverse urban images, including aquatic views. The results demonstrate that our approach effectively maintains the high segmentation accuracy of the original Cityscapes dataset for urban elements while successfully integrating marine features. Importantly, this is achieved without necessitating additional training, which is a significant advantage in terms of resource efficiency.

Beetle antennae search (BAS) is a newly developed meta-heuristic algorithm which is effectively used for optimizing objective functions of complex forms or even unknown forms. The common practice for implementing meta-heuristic algorithms including the BAS largely relies on programming in a high-level language and executing the code on a computer platform. However, the high-level implementation of the BAS algorithm hinders it from being used in an embedding system, where real-time operations are normally required. To address this limitation, we present an approach to implementing the BAS algorithm on a field-programmable gate array (FPGA). Specifically, we program the BAS function in the Verilog hardware description language (HDL), which provides a tractable vehicle for implementing the BAS algorithm at the gate level on the FPGA chip. We simulate our Verilog HDL based BAS module with the Modelsim platform. Simulation results validate the feasibility of our proposed Verilog HDL implementation of the BAS. Additionally, we implement the BAS model on the Zynq XC7Z010 platform, with $132.5~\mu $ s latency for model implementation.