Results in Engineering (Dec 2022)
SIDA-GAN: A lightweight Generative Adversarial Network for Single Image Depth Approximation
Abstract
SIDA-GAN is a set of GAN based deep learning models that can be employed for predicting the depth map for a given single input RGB image. This article explores the research gap present in the state of the art which lacks GAN based depth predictors implemented using Depthwise Separable Convolution (DSC). DSC reduces the number of computations in comparison with standard convolution for the same convolution task, therefore making the model computationally efficient. The adversarial loss applied by the GAN model make the predicted depth image globally coherent while loss functions like L1, SSIM fine tunes the local feature predictions leading to enhanced prediction quality. In this proposal, the depth predictor (SIDA-GAN generator) is an encoder-decoder design featuring DSC and skip connections. SIDA-GAN has two GAN variants namely conditional GAN (cGAN), patch GAN (pGAN) and two generator implementations based on MobileNetV1, MobileNetV2 leading to four different flavours. Those are SIDA-cGAN (MobileNetV1 based generator), SIDA-cGAN (MobileNetV2 based generator), SIDA-pGAN (MobileNetV1 based generator), SIDA-pGAN (MobileNetV2 based generator). The proposed models are trained with different combinations of SSIM, L1, depth-smoothness and adversarial losses using NYU-Depth V2 (Nathan Silberman et al., 2012) dataset. SIDA-GAN has achieved reduction in trainable parameter count up to 76% and performance improvement in Average Relative Error (8%–25%), Root Mean Square Error (3%–27%), threshold accuracy (2%–6%) when compared with baseline papers.
