GUV-Net for high fidelity shoeprint generation

Abstract

Shoeprints contain valuable information for tracing evidence in forensic scenes, and they need to be generated into cleaned, sharp, and high-fidelity images. Most of the acquired shoeprints are found with low quality and/or in distorted forms. The high-fidelity shoeprint generation is of great significance in forensic science. A wide range of deep learning models has been suggested for super-resolution, being either generalized approaches or application specific. Considering the crucial challenges in shoeprint based processing and lacking specific algorithms, we proposed a deep learning based GUV-Net model for high-fidelity shoeprint generation. GUV-Net imitates learning features from VAE, U-Net, and GAN network models with special treatment of absent ground truth shoeprints. GUV-Net encodes efficient probabilistic distributions in the latent space and decodes variants of samples together with passed key features. GUV-Net forwards the learned samples to a refinement-unit proceeded to the generation of high-fidelity output. The refinement-unit receives low-level features from the decoding module at distinct levels. Furthermore, the refinement process is made more efficient by inverse-encoded in high dimensional space through a parallel inverse encoding network. The objective functions at different levels enable the model to efficiently optimize the parameters by mapping a low quality image to a high-fidelity one by maintaining salient features which are important to forensics. Finally, the performance of the proposed model is evaluated against state-of-the-art super-resolution network models.

Deep Learning Based Detector YOLOv5 for Identifying Insect Pests

“…Visual information acquisition and processing via computer vision are inevitable to carry out pest detection and spot spraying. Therefore, deep neural networks (DNNs) are commonly used in computer vision applications to map complex correlations and to carry automatic feature extractions [11][12][13]. Advances in graphical processing units (GPUs) have enabled the training of deeper artificial neural networks with speedy and improved outcomes.…”