Design and implementation of suspension robot control system based on improved Yolov5
As deep learning technology continues to develop,more and more intelligent applica-tions are emerging.Hardware devices used for training and inference are typically based on GPUs,which are expensive and energy-intensive in practical applications.This paper addresses the issue of balancing cost and algorithm availability in existing deep learning systems by designing a rail-mounted robot control system with a Raspberry Pi 4b as the computing platform.The system ap-plies the improved Yolov5 algorithm model to realize the recognition and detection of pedestrian tar-gets.The improved model can achieve an accuracy of 89％when the target is a face.
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