Cascade single stage Detector using full convolution network

Convolution in Convolution for Network in Network

Network in network (NiN) is an effective instance and an important extension of deep convolutional neural network consisting of alternating convolutional layers and pooling layers. Instead of using a linear filter for convolution, NiN utilizes shallow multilayer perceptron (MLP), a nonlinear function, to replace the linear filter. Because of the powerfulness of MLP and 1 x 1 convolutions in spa...

New full adders using multi-layer perceptron network

How to reconfigure a logic gate for a variety of functions is an interesting topic. In this paper, a different method of designing logic gates are proposed. Initially, due to the training ability of the multilayer perceptron neural network, it was used to create a new type of logic and full adder gates. In this method, the perceptron network was trained and then tested. This network was 100% ac...

Single Image Deraining using Scale-Aware Multi-Stage Recurrent Network

Given a single input rainy image, our goal is to visually remove rain streaks and the veiling effect caused by scattering and transmission of rain streaks and rain droplets. We are particularly concerned with heavy rain, where rain streaks of various sizes and directions can overlap each other and the veiling effect reduces contrast severely. To achieve our goal, we introduce a scale-aware mult...

Diagonal-transition quantum cascade detector

3D-DETNet: a Single Stage Video-Based Vehicle Detector

Video-based vehicle detection has received considerable attention over the last ten years and there are many deep learning based detection methods which can be utilized to solve the problem. However, these methods are devised for still images and applying them for video vehicle detection directly always obtain poor performance. In this work, we propose a new one-stage video-based vehicle detect...