Real-time Vision-based System Research Articles

Autonomous Robot Navigation Using Adaptive Potential Field

This work presents a novel approach to mobile robot autonomous navigation, utilizing enhanced artificial potential fields in conjunction with path planning,obstacle avoidance and SLAM . In traditional adaptive potential fields, which integrate sensor data to enable robots to navigate safely and efficiently through various environments. The robot's perception of its surroundings, obtained through sensors like cameras, ultrasonic devices, is processed to create a dynamic potential field that reflects the spatial distribution of obstacles, goals, and other relevant features.We propose the design and implementation of an autonomous robot-based real-time vision-based system for detecting and tracking features in a structured environment ensuring robust obstacle avoidance and path planning. Keywords- Adaptive Potential Fields, Autonomous Navigation, Autonomous Systems, Machine Learning in Robotics, Motor driver, Obstacle Avoidance, Path Planning, Raspberry Pi, Real-time Adaptation, ROS (Robot Operating System), Sensor-based Navigation, Servo Motor, SLAM (Simultaneous Localization and Mapping),Ultrasonic Sensors

Inspection of Cotton Woven Fabrics Produced by Ethiopian Textile Factories Through a Real-Time Vision-Based System

ABSTRACT Fabric is produced by the weaving process through the interlacement of warp and weft yarn or knitting process through the loop formation of yarn. During these processes, there is a possibility of fabric defect formation which hinders the acceptability by the fabric consumers. Ethiopian textile factories practiced a human inspection system, a traditional means of detecting fabric quality, for monitoring textile fabric defects. Manual fabric defect detection helps to instantly correct small defects, but it is time-consuming and results in human error due to fatigue and lack of concentration. Moreover, the accuracy of recognizing the defect highly depends on the mental status of the person that checks the defects. This initiated the development of a better fabric defect identification system that helps textile experts to detect fabric defects with better precision and speed. This study proposes a vision-based fabric inspection system for plain woven gray fabrics with a uniform texture. Accordingly, a comprehensive Fabric Defect Detection Database (FDDD) is constructed. The fabric significant features were calculated using a convolutional neural network (CNN) which is a state-of-the-art technology in image processing and task analysis. The experimental result of this study shows an average accuracy of about 89% in fabric defect recognition.

AN ARTIFICIAL INTELLIGENCE-BASED SPEAKING SYSTEM FOR DUMB PEOPLE

For deaf-mute people all around the world, Gestures are the main mode of communication for DeafMute people worldwide. In this gesture-based voice system, machine learning presents a real-time vision-based system for monitoring hand finger gestures. It was developed in Python using Raspberry Pi along with a camera module, which is compatible with the Open CV library for computer vision. The Raspberry Pi includes an image processing technique that monitors the fingers of the hand using the extracted attributes. The main purpose of a gesture based speaking system is to develop control communication between humans and computers. This leads to a system that can recognize and monitor known objects and has surveillance and application capabilities. The major goal of the suggested work was to allow the system to function properly. The main objective is to enable the system to recognize and monitor certain properties of objects specified by the Raspberry Pi along with camera module using an appropriate image processing technique. The Open CV library's feature extraction technique of the Open CV library for Python programming runs on Raspberry Pi using an external camera. A gesture based speaking system using machine learning provides a new, intuitive, and simple way to communicate with computers that are more human-like.

A Lightweight NMS-Free Framework for Real-Time Visual Fault Detection System of Freight Trains

Real-time vision-based system of fault detection (RVBS-FD) for freight trains is an essential part of ensuring railway transportation safety. Most existing vision-based methods still have high computational costs based on convolutional neural networks. The computational cost is mainly reflected in the backbone, neck, and post-processing, i.e., non-maximum suppression (NMS). In this paper, we propose a lightweight NMS-free framework to achieve real-time detection and high accuracy simultaneously. First, we use a lightweight backbone for feature extraction and design a fault detection pyramid to process features. This fault detection pyramid includes three novel individual modules using attention mechanism, bottleneck, and dilated convolution for feature enhancement and computation reduction. Instead of using NMS, we calculate different loss functions, including classification and location costs in the detection head, to further reduce computation. Experimental results show that our framework achieves over 83 frames per second speed with a smaller model size and higher accuracy than the state-of-the-art detectors. Meanwhile, the hardware resource requirements of our method are low during the training and testing process.

Real-Time Vision-Based System of Fault Detection for Freight Trains

Real-time vision-based system of fault detection (RVBS-FD) for freight trains aims to complete routine maintenance tasks efficiently for ensuring railway transportation security. However, most existing systems are designed to detect only one specific type of faults or even one fault, which fail to deal with multifault detection. Recently, the rapid development in deep learning techniques enables systems to provide a robust solution for the RVBS-FD of freight trains. However, general convolutional neural networks (CNNs) cannot fully meet the actual requirements in terms of the real time, accuracy, and resource constrains for the RVBS-FD of freight trains. To solve these problems, we propose a CNN-based detector called Light fault detection network for freight train images (FTI-FDNet) for the RVBS-FD of the freight train. First, we use the multiregion proposal networks (MRPN), which extract a set of prior bounding boxes to achieve initial fault proposal generation. Then, a powerful multilevel region-of-interest (RoI) pooling is presented for proposal classification and accurate detection. We finally design a reliable model reduction scheme (MRS) to pursue fast speed with high detection accuracy in a simple manner. The experimental results on five typical fault benchmarks indicate that our Light FTI-FDNet achieves higher accuracy and fast speed with about 17% model size of the well-known faster region-based CNN (R-CNN) detector, substantially outperforming the state-of-the-art methods.

Real-time vision-based eye state detection for driver alertness monitoring

This paper presents a real-time vision-based system to detect the eye state. The system is implemented with a consumer-grade computer and an uncalibrated web camera with passive illumination. Previously established similarity measures between image regions, feature selection algorithms, and classifiers have been applied to achieve vision-based eye state detection without introducing a new methodology. From many different extracted data of 1,293 pair of eyes images and 2,322 individual eye images, such as histograms, projections, and contours, 186 similarity measures with three eye templates were computed. Two feature selection algorithms, the $$ J_{5} (\xi ) $$ criterion and sequential forward selection, and two classifiers, multi-layer perceptron and support vector machine, were intensively studied to select the best scheme for pair of eyes and individual eye state detection. The output of both the selected classifiers was combined to optimize eye state monitoring in video sequences. We tested the system with videos with different users, environments, and illumination. It achieved an overall accuracy of 96.22 %, which outperforms previously published approaches. The system runs at 40 fps and can be used to monitor driver alertness robustly.

Real time feature extraction and Standard Cutting Models fitting in grape leaves

In food industry, a continuous effort is made to automate the production lines and, in many cases, it is desired to automate the plant leaves processing. In this paper we present a real-time vision-based system for main features extraction of grape leaves and for positioning and fitting of Standard Cutting Models (SCMs) onto leaves’ profiles. The system comprises of a real-time software application connected to a fixed camera that captures still images of grape leaves, as they are moving on a conveyor belt. Our research focused on combining existing and novel, application specific, algorithms for image processing and developing the software application in visual C++. The aim was to create a reliable system that would process grape leaves automatically and in real time, reducing the wastage and enabling the optimization of the whole process in an industrial production line. Experimental results are also presented, in order to verify and confirm the efficiency and effectiveness of the proposed system.

Automated handwashing assistance for persons with dementia using video and a partially observable Markov decision process

This paper presents a real-time vision-based system to assist a person with dementia wash their hands. The system uses only video inputs, and assistance is given as either verbal or visual prompts, or through the enlistment of a human caregiver’s help. The system combines a Bayesian sequential estimation framework for tracking hands and towel, with a decision-theoretic framework for computing policies of action. The decision making system is a partially observable Markov decision process, or POMDP. Decision policies dictating system actions are computed in the POMDP using a point-based approximate solution technique. The tracking and decision making systems are coupled using a heuristic method for temporally segmenting the input video stream based on the continuity of the belief state. A key element of the system is the ability to estimate and adapt to user psychological states, such as awareness and responsiveness. We evaluate the system in three ways. First, we evaluate the hand-tracking system by comparing its outputs to manual annotations and to a simple hand-detection method. Second, we test the POMDP solution methods in simulation, and show that our policies have higher expected return than five other heuristic methods. Third, we report results from a ten-week trial with seven persons moderate-to-severe dementia in a long-term care facility in Toronto, Canada. The subjects washed their hands once a day, with assistance given by our automated system, or by a human caregiver, in alternating two-week periods. We give two detailed case study analyses of the system working during trials, and then show agreement between the system and independent human raters of the same trials.