Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic map for service robot navigation based on ROS

The environment perception and navigation ability of robots are the basis of robot interaction with environment. The scene understanding is the prerequisite of robot autonomous navigation and navigation is the ultimate goal of robot scene understanding, therefore, a mobile robot semantic map navigation system is designed. Firstly, a dense pointcloud map is constructed by using a deep convolutional neural network. Then, the mapping relationship between the depth camera data and the laser data is proposed, for purpose of realizing their interconversion. And a method of constructing two-dimensional raster map by simulating laser data by using the data of the depth camera is proposed. Finally, a method that semantic information is integrated into the two-dimensional grid map is proposed, realizing the semantic navigation of the service robot. The experimental results showed that compared with other robotic navigation systems, the proposed algorithm can better realize the interactions between service robots and people and the environment.
Semantic segmentation of buildings in high resolution remote sensing images using conditional random fields

This paper proposes a method of building a semantic segmentation method for high-resolution remote sensing images of conditional random fields. Through a large number of actual data operations comparison, U-Net semantic segmentation model is selected as the improved basic model in many deep convolutional neural network models. In order to improve the singularity of the upsampling operation, the U-Net semantic segmentation model is improved as follows: First, the model's crop-copy connection structure is changed to the pyramid pooling layer, and then the multi-scale representation feature image is used, and the multi-scale is used. The resampling of the feature image and the fine bilinear interpolation yield the maximum response at different scales. The improved U-Net model extracts more complete image features. The rough segmentation results are used as the initial input values of the fully connected conditional random fields (CRFs). The global pixel potential energy is inferred through the fully connected graph, and the feature images are refined. Target matching. Finally, the image features are input to the sigmoid classifier for analysis. The results show that the CRF-SUNet model with introduced conditional random field has high segmentation precision, and the boundary of the segmented building is clear, smooth and complete.
Multi-scales feature integration single shot multi-box detector on small object detection

SSD (Single Shot Multi-box Detector) is one of the best object detection algorithms with both high accuracy and fast speed. However, SSD’s feature pyramid detection method only extracts the features from different scales without further procession, which leads to semantic information lost. In this paper, we proposed Multi-scales Feature Integration SSD, an enhanced SSD with feature integrated modules which can improve the performance significantly over SSD. In the feature integrated modules, features from different layers with different scales are concatenated together after some upsampling tricks, then we use the features as input of several convolutional modules, those modules will be fed to multibox detectors to predict the final results. We test our algorithm On the Pascal VOC 2007test with the input size 300×300 using a single Nvidia 1080Ti GPU. In addition, our network outperforms a lot of state-of-the-art object detection algorithms in both aspects of accuracy and speed.
A deblurring model for super-resolution MRI interpolated images

In the up-sampling process may occur effects like aliasing, blurring or noise addition which mainly affect the edges of the images. For those reasons is necessary to choose a method that preserves images quality so that these problems are minimized. In this paper, we present an alternative method to restore blurred images using linear programming to solve a minimization problem stated in the L1 norm. The model requires the blurred image and some prior knowledge about the blurring function type (Point spread function). In the proposed method we obtain a PSNR of 30 dB overcoming a classic bi-linear method by 4 dB in a set of thirty images from a cardiac MRI data set.
Dynamic low-frequency fluctuations of resting brain in attention deficit hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition whose mechanism is not completely understood. However, it has been consistently found that regions within the Default Mode Network have an important role in the dysfunctional brain activity. The objective of this study was to use a dynamic brain approach for the analysis of resting-state functional magnetic resonance imaging data from subjects with typical development and subjects with ADHD, and to compare it with the static approach. In the current study we computed the amplitude of low frequency fluctuations in the 0.01 to 0.08 Hz frequency band within the Default Mode Network regions. Statistically significant differences between typical development and ADHD subjects were found, where more regions revealed a differential behavior between groups when performing the dynamic approach. Results indicated that the dynamic approach provides complementary explanation of brain function, compared to the static analysis, and allows to access information concerning temporal behavior by looking at each window.
Integrated convolutional neural network model with statistical moments layer for vehicle classification

Vehicle classification is an important topic which is still under research consideration because of its role in road surveillance, security system, traffic monitoring, and accident prevention. In this paper, we propose a deep learning model for vehicles classification using the Convolutional Neural Networks (CNN) integrated with a statistical moments layer. We referred to the model as ICNN. As an additional layer, the moments layer extracts statistical moments features from the feature maps obtained from convolutions layers. The moments layer is fed the fully-connected classifier of the network which is fine-tuned to get better results. Our Integrated CNN model (ICNN) achieves 97.1% accuracy compared to the most popular algorithms used in this field such as K Nearest Neighbour (KNN), and Support Vector Machine (SVM), which known as good tools for object classification.
Method of quality assessment based on convolution feature similarity for laser disturbing image

At present, most of the full reference laser disturbing image quality assessment methods need to know the position information of the disturbing spot and the target in advance, so that the assessment process is restricted by the prior knowledge and the preprocessing method. Aiming at this problem, this paper proposes a laser disturbing image quality assessment method based on convolution feature similarity (CNNSIM), which analyzes the output features of the image before and after laser disturbing in the convolution network. The occlusion degree of key information in the disturbing image is assessed by using the hierarchy and the sensitivity to occlusion of features, thus avoiding the input requirement of target/spot location information. The simulation experiment verifies the effectiveness of the new assessment method in different scenarios.
Estimation of extent of trees and biomass infestation of the suburban forest of Thessaloniki (Seich Sou) using UAV imagery and combining R-CNNs and multichannel texture analysis

Given the urgent priority around protecting the forests and limiting the impacts of the climate change, the constant monitoring of forests towards the achievement of accurate and timely detection of infestations and the catastrophic action of invasive insects, pests and fungi is an important and challenging task. More precisely, new species of insects that are introduced or already existing insect species whose population multiply uncontrollably into the forest area, affect tree growth, their survival, as well as the quality of forest biomass and constitute a serious threat to the mechanisms of such forest ecosystems. Thus, new concepts are needed that will overcome difficulties faced by existing remote sensing techniques and that would allow the timely and accurate health determination process of forest regions, assisting scientists and authorities to take action in order to protect the forests. In this paper, we propose a monitoring approach, which uses high resolution RGB aerial images and combines different Region Convolution Neural Networks (R-CNNs) architectures, namely Faster R-CNN and Mask R-CNN and fuses their bounding box outcomes in order to more accurately localize candidate infected trees’ regions whilst increasing the number of the candidate trees that have been detected as infected. Subsequently, the candidate detected trees are modelled through the higher order linear dynamical systems (h-LDS) and descriptors are extracted for each candidate region. Finally, the h-LDS descriptors are classified using an SVM classifier for the estimation of the infected trees. The study area includes parts of the suburban pine forest of Thessaloniki city (Greece) named Seich Sou, which suffers the last months an infestation of high significance and intensity by a bark and wood destroying insect (Tomicus piniperda). Although this insect was recorded in the specific ecosystem many years ago, its population increased uncontrollably after the degradation of the ecosystem due to human intervention and lack of protection and management strategy. Experimental results, through their outperforming existing state-of-the-art algorithms, demonstrate high potential and perspectives of the proposed methodology of low cost and time consumed, to contribute to the sustainable management, protection and recovery of a forest ecosystem.
Supervised learning for semantic segmentation of human spermatozoa

Image-based diagnosis is able to spot several diseases and clinical conditions faster and more accurately than traditional manual ones, becoming also an alternative in monitoring and predicting patients responses to specific health treatments. In this work, we present a supervised learning approach to segment pixel-wise parts of spermatozoa using a random forest (RF) classifier. The framework created a multi-channel image combining intensity RGB bands with three neighborhood based bands. The last neighborhood based bands were Sobel’s magnitude and orientation and Shannon’s entropy. A RF was trained using labeled pixels provided by expert andrologists, biochemists and specialists in reproductive health. We compared results with a simple model on the RGB only. The whole automatic process (segmentation and classification) achieved an average precision of 98%, recall of 98% and F-Score of 98%. Highest improvement in comparison to the RGB model was shown on the segmentation of the tail. We provided a fully automatic spermatozoa semantic segmentation based on local and non-local information. The results are aimed to develop a CASA (Computer Assisted Sperm Analysis) system that can provide results over the Internet. The experiment was conducted on normalized images of a specific microscope. We are planning to extend the experiment in future work to more realistic conditions including different stainings, microscopes and resolutions.
A CNN-based method for adaptive landmark selection in remote sensing image

The technology of automatic selecting landmark plays a significant role in aircraft navigation and ground information assurance. Compared to the normal object detection, it is quite difficult to describe and quantify the characteristics of a landmark due to its various status and no stable structure. This paper attempts to innovatively combine CNN with the technology of selecting landmark. The algorithm used in this paper uses a structurally stable adaptation region as a learning sample to train the CNN classification model. In the selection phase, remote sensing images were cut into pieces of patches, landmark of which was then recognized through the CNN classification model. Non-maxima suppression was used to filter out the low rate landmark and a correlation peak-based uniqueness analysis (the ratio of primary and secondary peaks and the highest sharpness of peak) was used to ensure landmark with no similarity pattern in the remote sensing image. The results indicate the effectiveness of proposed method for Selecting Remote Sensing Image Adaptation Structure.
LIDAR-based SLAM implementation using Kalman filter

The capability to navigate accurately is one of the features, that a mobile robot should have to be able to perform tasks autonomously. In a GPS/GNSS-denied environment, for example inside buildings, localization of a mobile platform is an especially challenging problem. In such cases, to provide a robot with the ability to determine its position and to analyze its surroundings, Simultaneous Localization and Mapping (SLAM) algorithms could be implemented. In the article, we present a SLAM system that uses a Kalman filter together with data gathered by a 2D LiDAR. Our approach applies the ICP algorithm to calculate the localization and employs clustering and shape recognition technics to build the map of the environment. The article contains a detailed description of the individual elements of the proposed SLAM solution. Furthermore, it presents the results of experiments during which the system was validated.
Hypothalamus fully automatic segmentation from MR images using a U-Net based architecture

Hypothalamus is a small structure of the brain with an important role in sleep, appetite, body temperature regulation and emotion. Some neurological diseases, such as Schizophrenia, Alzheimer and Amyotrophic Lateral Sclerosis (ALS) may be related to hypothalamic volume variation. However, hypothalamic morphological landmarks are not always clear on magnetic resonance (MR) images and manual segmentation can become variable, leading to inconsistent findings in the literature. In this work, we propose a fully automatic segmentation method, with no human interaction, to segment hypothalamus in MR images using convolutional neural networks (CNNs). The best performance was obtained by a consensus model using the majority voting from three 2D-CNNs trained on axial, coronal and sagittal MRI slices, achieving a DICE coefficient of 0.77. To the best of our knowledge, this is the first work to fully automatically segment the hypothalamus.
Reference image generation for space target recognition on ground-based observation

Space object observation by using small ground-based telescopes has become a common supplementary means for usual space object observation because of its flexible operation and convenient carrying. However, due to atmospheric turbulence, urban lighting and other interference factors, the low resolution, blurred contour and scarce texture of the small ground-based telescope space target observation image seriously affect the space target recognition. At the same time, there are few real-time data for space target observation, thus the conventional deep learning method which needs lots of offline trainning data cannot be applicated. Therefore, it is necessary to prepare typical space target reference image by means of three-dimensional model and simulation image. Generative adversarial network provides a simulation way to prepare training data offline. In the preparation of generation images of space target recognition for ground-based observation, the 3D model data of space target are projected in two dimensions by setting elevation angle, roll angle and spin angle. And the two-dimensional generation images of space target are obtained. Then, GAN is trained by using real-time observation data of some existing space targets and corresponding two-dimensional generation images as input, and simulation generation images of typical space targets are obtained by using two-dimensional datum map on trained GAN. Experiments show that the reference images simulated by GAN are more authentic than those generated by traditional methods, and have higher correct rate, accuracy and efficiency on recognition mission.
The fault diagnosis of catenary system based on the deep learning method in the railway industry

The catenary system plays a vital role in the railway industry, which is associated with the security and efficiency of the train operation. The fault diagnosis and anomaly detection of the catenary system is of significance. The current carrying ring and dropper are important parts of catenary and attract attention in the inspection process. Based on the image processing technique and deep learning method, the fault diagnosis method of the catenary system is presented. The fault diagnosis of catenary system consists of three parts, top current carrying ring, dropper and bottom current carrying ring detection. The feature pyramid network is applied for the various scales units of catenary system in image from inspection vehicle. Based on the modified CenterNet, the current carrying ring is detected. The results of the located rings are chosen through specific selection. Then the selected top and bottom rings are matched further through the location relationship. Based on the matched rings, the dropper is located and then classified by the classification network. According to the experiments on the plenty of catenary image datasets, it shows that the method have efficient and satisfied performance on the fault diagnosis of the catenary system.
Preliminary design for Earth observation video satellites to apply predictable trajectory parameters in video compression techniques

One of the available capacities of satellite application is applying in Earth Observation (EO) video capture satellites. On the one hand, the governing equations of satellite motion in orbit indicate the fact that a satellite moves in a very predictable manner and stays on schedule. On the other hand, the superiority of Intra and Inter codings, drive video compressors to reduce the spatial redundancy and data rate. This advantage stems from the flexible coding structure and high density of angular prediction modes, in all video compression versions. How to combine these facts to achieve better performance in industrial satellite applications is our aim in this article. In this study, a novel architectural approach for EO video capture satellites that have taken the new demands of the next commercial market has been proposed. Here after looking at video coding parameters, technical EO satellite essentialities, and proving the proposed hypothesis by simulation and evidence, a preliminary configuration has been described. The Attitude and orbit control systems (AOCS) are responsible for keeping the satellite position in the required condition contributing to Onboard Data Handling (OBDH). The exact parameters of direction are key factors that can be used besides video prediction vector data to reduce calculation load in video compression algorithms that particularly explained.
Smoke detection in infrared images based on superpixel segmentation

Infrared smoke interference technology seriously infected the combat effectiveness of photoelectric guided weapons in modern warfare. As a result of the occlusion caused by smoke screen, the robustness of image matching guidance algorithm will decrease. Thus, to judge whether there is smoke interference in images and smoke screen area extraction are of great importance for the accuracy of image matching guidance algorithm. However, most of the smoke detection methods aimed at fire early warning, so that they focused on whether smoke exists or not. While both of the discrimination of smoke interference and smoke screen area extraction are what we concern. In this paper, a smoke detection method based on superpixel segmentation and region merging is proposed. Firstly, over-segmentation regions of input infrared image with superpixel segmentation are obtained. Then, fusion texture feature of the image is computed. Finally, superpixel regions are merged based on the fusion features of each superpixel block obtained in the previous step and smoke screen area extraction is completed.
Dimensionality reduction of hyperspectral images based on the linear mixture model and dimensionality estimation

In this paper, we propose a dimensionality reduction technique, which is based on the principal component analysis of homogenous spatial regions of hyperspectral images. In the proposed technique, we rely on the linear mixture model and use a dimensionality estimation procedure to split an image into homogenous regions. The experiments carried out using well-known hyperspectral image scenes show that the proposed technique allows obtaining compact representations of image regions in reduced spectral subspaces and can be considered as a segmentation technique.
Pose estimation for non-cooperative targets using 3D feature correspondences grouped via local and global constraints

The pose of a non-cooperative target represented by point cloud can be estimated through point cloud registration, which is generally performed by searching good correspondences. Seeking correspondences in the context of non-cooperative target pose estimation is a challenging task due to the low texture, noise and occlusion, resulting in a number of outliers in the initial correspondences. In order to gain a high quality set of feature correspondences, we employ a combination of local and global constraints to remove the outliers in initial correspondences. On a local scale, we use simple and low-level geometric invariants. On a global scale, we apply covariant constraints for finding compatible correspondences. In the experiments, we use four groups of different non-cooperative targets to evaluate our algorithm and the results verify that the quality of the correspondence set has been greatly improved by our method and the pose can be accurately estimated.
Research on intelligent recognition of weak human target in land and sea

Great progresses have been achieved on object recognition in the wild. However, it still remains a challenging problem due to tiny objects. Likewise, post-disaster or post-war personnel search and rescue missions often encounter specific problems of complex and wide areas and weak human targets. This work proposes a weak human target recognition technique for unmanned personnel search and rescue. Firstly, referring to human visual cognitive mechanism based on visible light detection equipment, deep convolutional neural network is applied to enable the model to perceive the targets in complex background. Then a single-shot detector is designed to extract the scale adaptive depth feature to match the regions of interest. Last, a deep network model compression algorithm and an efficient online processing framework are designed to speed up and detect the targets.