Image-free real-time classification of fast moving objects using 'learned' spatial light modulation and a single-pixel detector

TL;DR

This paper introduces a novel real-time method for classifying fast-moving objects without image acquisition, using spatial light modulation and a single-pixel detector combined with a neural network to encode and classify features efficiently.

Contribution

It presents a new approach that directly encodes object features via spatial light modulation and neural networks, eliminating the need for image capture in real-time classification.

Findings

Achieves accurate real-time classification of fast-moving objects.

Uses structured light patterns for compressive feature encoding.

Demonstrates effectiveness with experimental validation.

Abstract

Objects classification generally relies on image acquisition and analysis. Real-time classification of high-speed moving objects is challenging, as both high temporal resolution in image acquisition and low computational complexity in objects classification algorithms are required. Here we propose and experimentally demonstrate an approach for real-time moving objects classification without image acquisition. As objects classification algorithms rely on the feature information of objects, we propose to use spatial light modulation to acquire the feature information directly rather than performing image acquisition followed by features extraction. A convolutional neural network is designed and trained to learn the spatial features of the target objects. The trained network can generate structured patterns for spatial light modulation. Using the resulting structured patterns for spatial light modulation, the feature information of target objects can be compressively encoded into a short light intensity sequence. The resulting one-dimensional signal is collected by a single-pixel detector and fed to the convolutional neural network for objects classification. As experimentally demonstrated, the proposed approach can achieve accurate and real-time classification of fast moving objects. The proposed method has potential applications in the fields where fast moving objects classification in real time and for long duration is required.