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  • Open Access

    ARTICLE

    Design of a Patrol and Security Robot with Semantic Mapping and Obstacle Avoidance System Using RGB-D Camera and LiDAR

    Shu-Yin Chiang*, Shin-En Huang

    CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074528 - 10 February 2026

    Abstract This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping (SLAM), real-time object recognition, and dynamic obstacle avoidance. The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping, fusing geometric and visual data to build a high-fidelity 2D semantic map. This map enables the robot to identify and project object information for improved situational awareness. Experimental results show that object recognition reached 95.4% mAP@0.5. Semantic completeness increased from 68.7% (single view) to 94.1% (multi-view) with an More >

  • Open Access

    ARTICLE

    Underwater Waste Recognition and Localization Based on Improved YOLOv5

    Jinxing Niu1,*, Shaokui Gu1, Junmin Du2, Yongxing Hao1

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2015-2031, 2023, DOI:10.32604/cmc.2023.040489 - 30 August 2023

    Abstract With the continuous development of the economy and society, plastic pollution in rivers, lakes, oceans, and other bodies of water is increasingly severe, posing a serious challenge to underwater ecosystems. Effective cleaning up of underwater litter by robots relies on accurately identifying and locating the plastic waste. However, it often causes significant challenges such as noise interference, low contrast, and blurred textures in underwater optical images. A weighted fusion-based algorithm for enhancing the quality of underwater images is proposed, which combines weighted logarithmic transformations, adaptive gamma correction, improved multi-scale Retinex (MSR) algorithm, and the contrast… More >

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