Detecting Object Open Angle and Direction Using Machine Learning

Nowadays, the object detection techniques have been developed rapidly for different applications, ranging from remote sensing to autonomous vehicles. We demonstrate identification of object open angle and direction using machine learning (ML) algorithms based on received light beam's intensity profiles. Compared with previous optical orbital-angular-momentum (OAM) spectrum system and other related works, our proposed technique only uses a single-shot image, and can efficiently reduce the complexity of hardware implementation. Specifically, we verify the reliability of the simulation results experimentally for 14 open angles and 32 directions. Experimental result shows that convolutional neural network (CNN) outperforms the other traditional ML algorithms, such as decision tree (DT), k-nearest neighbor algorithm (KNN), and support vector machine (SVM). As one of the variant of CNN, MobileNet (MN) has relatively simplified iteration algorithm than VGG-like net. It reduces the computational power, while still maintaining high accuracy for identification issues.

Abstract licence: CC BY-NC-ND

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