Abstract
Objectives: This study aims to design a dead-angle-free smart desk lamp. Methods: The convolutional neural network (CNN) algorithm was used to realize the identification and positioning of objects. Then, the desk lamp arm was driven according to positioning to realize dead-angle-free illumination. In the subsequent testing, the designed desk lamp was compared with others driven by the support vector machine (SVM) and back-propagation neural network (BPNN) algorithms. Findings: The CNN algorithm implemented in the smart desk lamp demonstrated superior target recognition performance and positioning accuracy when compared to the other two algorithms. Moreover, with this algorithm, the smart desk lamp efficiently generated tracking responses for targets and displayed minimal positioning errors once tracking became stable. Novelty:The novelty of this article lies in the utilization of the CNN algorithm to achieve visual tracking for a smart desk lamp, which serves as the basis for its automatic adjustment. Doi: 10.28991/HIJ-2023-04-04-05 Full Text: PDF
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