Robust multi-class model constructed for rapid quality control of Cordyceps sinensis

End-to-end integrated pipeline for underwater optical signal detection using 1D integral imaging capture with a convolutional neural network

Underwater optical signal detection performance suffers from occlusion and turbidity in degraded environments. To tackle these challenges, three-dimensional (3D) integral imaging (InIm) with 4D correlation-based and deep-learning-based signal detection approaches have been proposed previously. Integral imaging is a 3D technique that utilizes multiple cameras to capture multiple perspectives of the scene and uses dedicated algorithms to reconstruct 3D images. However, these systems may require high computational requirements, multiple separate preprocessing steps, and the necessity for 3D image reconstruction and depth estimation of the illuminating modulated light source. In this paper, we propose an end-to-end integrated signal detection pipeline that uses the principle of one-dimensional (1D) InIm to capture angular and intensity of ray information but without the computational burden of full 3D reconstruction and depth estimation of the light source. The system is implemented with a 1D camera array instead of 2D camera array and is trained with a convolutional neural network (CNN). The proposed approach addresses many of the aforementioned shortcomings to improve underwater optical signal detection speed and performance. In our experiment, the temporal-encoded signals are transmitted by a light-emitting diode passing through a turbid and partial occluded environment which are captured by a 1D camera array. Captured video frames containing the spatiotemporal information of the optical signals are then fed into the CNN for signal detection without the need for depth estimation and 3D scene reconstruction. Thus, the entire processing steps are integrated and optimized by deep learning. We compare the proposed approach with the previously reported depth estimated 3D InIm with 3D scene reconstruction and deep learning in terms of computational cost at receiver's end and detection performance. Moreover, a comparison with conventional 2D imaging is also included. The experimental results show that the proposed approach performs well in terms of detection performance and computational cost. To the best of our knowledge, this is the first report on signal detection in degraded environments with computationally efficient end-to-end integrated 1D InIm capture stage with integrated deep learning for classification.