Optimization of YOLO for real-time object detection in robotic applications
Ran Wang
University of Shanghai for Science and Technology
DOI: https://doi.org/10.59429/esta.v12i1.9657
Keywords: Robotics; YOLO algorithm; Object detection; Optimization strategies
Abstract
With the rapid development of robotics technology, real-time object detection has become increasingly crucial in robotic application scenarios. YOLO (You Only Look Once), as an efficient object detection algorithm, has been widely applied in the field of robotics. However, in practical applications, it still faces issues such as the balance between speed and accuracy, and poor performance in detecting small objects. This paper delves deeply into the optimization strategies for YOLO real-time object detection in robotic applications. By improving the network structure, optimizing the training algorithm, and adopting multi-modal data fusion and other methods, it aims to enhance the performance of the YOLO algorithm in robotic applications, providing strong support for robots to achieve accurate perception and decision-making in complex environments.
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