Robot for plastic garbage recognition

Waste and related threats are becoming more and more severe problems in environmental security. There is growing attention in waste management globally, both in developing techniques to decrease their quantity and those correlated to their neutralization and commercial use. The basic segregation process of waste due to the type of material is insufficient, as we can reuse only some kinds of plastic. There are difficulties with the effective separation of the different kinds of plastic; therefore, we should develop modern techniques for sorting the plastic fraction. One option is to use deep learning and a convolutional neural network (CNN). The main problem that we considered in this article is creating a method for automatically segregating plastic waste into seven specific subcategories based on the camera image. The technique can be applied to the mobile robot for gathering waste. It would be helpful at the terrain and the sorting plants. The paper presents a 15-layer convolutional neural network capable of recognizing seven plastic materials with good efficiency

Deep Learning for Plastic Waste Classification System

Plastic waste management is a challenge for the whole world. Manual sorting of garbage is a difficult and expensive process, which is why scientists create and study automated sorting methods that increase the efficiency of the recycling process. The plastic waste may be automatically chosen on a transmission belt for waste removal by using methods of image processing and artificial intelligence, especially deep learning, to improve the recycling process. Waste segregation techniques and procedures are applied to major groups of materials such as paper, plastic, metal, and glass. Though, the biggest challenge is separating different materials types in a group, for example, sorting different colours of glass or plastics types. The issue of plastic garbage is important due to the possibility of recycling only certain types of plastic (PET can be converted into polyester material). Therefore, we should look for ways to separate this waste. One of the opportunities is the use of deep learning and convolutional neural network. In household waste, the most problematic are plastic components, and the main types are polyethylene, polypropylene, and polystyrene. The main problem considered in this article is creating an automatic plastic waste segregation method, which can separate garbage into four mentioned categories, PS, PP, PE-HD, and PET, and could be applicable on a sorting plant or home by citizens. We proposed a technique that can apply in portable devices for waste recognizing which would be helpful in solving urban waste problems

The use of Artificial Intelligence for Automatic Waste Segregation in the Garbage Recycling Process

The project financed under the program of the Polish Minister of Science and Higher Education under the name Regional Initiative of Excellence in the years 2019-2023 project number 020/RID/2018/19 the amount of financing PLN 12 000 000.The problem of recycling secondary raw materials remains unresolved, despite many years of work on this issue. Among the many obstacles that arise is also the difficulty of sorting individual waste fractions. To facilitate this task and help solve this problem, modern computer vision and artificial intelligence techniques can be used. In our work, we propose constructing an intelligent garbage bin containing a camera and a microcomputer along with software that uses these techniques to sort waste. The role of the software is to recognize the type of waste and assign it to one of five main categories: paper, plastic, metal, glass and cardboard. The proposed method uses image recognition techniques with a convolutional neural network. The results confirm that using artificial intelligence methods significantly helps in sorting waste

The use of hidden Markov models to verify the identity based on facial asymmetry

Abstract This work concerns the use of biometric features, resulting from the look of a face, for the verification purposes. Different methods of selection and feature analysis during face recognition are presented here. The description contains mainly analysis possibilities and also identity verification based on asymmetric facial features—in later stages. The new verification method has been introduced based on designated characteristic points. These points were designated through appropriate coded information about facial asymmetry as observation vectors and recognition using hidden Markov models. The advantage of these models is that we can use vectors of different lengths. Such vectors appear in incomplete data when all points cannot be located

Road signs recognition with two-dimensional hidden Markov models

Abstract. The automatic road sign recognition system is presented. The system uses two-dimensional hidden Markov models. The system is able to recognize the road signs, which were detected earlier in the image. The system uses wavelet transform for features extraction of road signs. In recognition process system uses two dimensional hidden Markov models. The experimental results demonstrate that the system is able to gain an average recognition rate of 83%. Streszczenie. Zaprezentowano Introduction For last years many researches in the field of automatic road sign detection and recognition systems were made. The main object of such systems is to warn a driver of presence of road signs, because he may not notice the presence of sign. The main problem is caused by the variable lighting conditions of a scene in a natural environment, and an automatic system should be able to detect signs in different conditions and position

Device for Acoustic Support of Orientation in the Surroundings for Blind People

The constant development of modern technologies allows the creation of new and, above all, mobile devices supporting people with disabilities. All work carried out to improve the lives of people with disabilities is an important element of the field of science. The work presents matters related to the anatomy and physiology of hearing, imaginative abilities of blind people and devices supporting these people. The authors elaborated a prototype of an electronic device that supports the orientation of blind people in the environment by means of sound signals. Sounds are denoted to present to a blind person a simplified map of the depth of space in front of the device user. An innovative element of the work is the use of Kinect sensor, scanning the space in front of the user, as well as a set of developed algorithms for learning and generating acoustic space, taking into account the inclination of the head. The experiments carried out indicate the correct interpretation of the modeled audible signals, and the tests carried out on persons with impaired vision organs demonstrate high efficiency of the developed concept

Detection of Dangerous Situations Near Pedestrian Crossings using In-Car Camera

e project financed under the program of the Pol ish Minister of Science and Higher Education under the name "Regional Initiative of Excellence" in the years 2019 - 2023 project number 020/RID/2018/19 the amount of financing PLN 12,000,000The paper presents a method for detecting dangerous situations near pedestrian crossings using an in-car camera system. The approach utilizes deep learning-based object detection to identify pedestrians and vehicles, analyzing their behavior to identify potential hazards. The system incorporates vehicle sensor data for enhanced accuracy. Evaluation results show high accuracy in detecting dangerous situations. The proposed system can potentially enhance pedestrian and driver safety in urban transportation

A Method of Speech Coding for Speech Recognition Using a Convolutional Neural Network

This work presents a new approach to speech recognition, based on the specific coding of time and frequency characteristics of speech. The research proposed the use of convolutional neural networks because, as we know, they show high resistance to cross-spectral distortions and differences in the length of the vocal tract. Until now, two layers of time convolution and frequency convolution were used. A novel idea is to weave three separate convolution layers: traditional time convolution and the introduction of two different frequency convolutions (mel-frequency cepstral coefficients (MFCC) convolution and spectrum convolution). This application takes into account more details contained in the tested signal. Our idea assumes creating patterns for sounds in the form of RGB (Red, Green, Blue) images. The work carried out research for isolated words and continuous speech, for neural network structure. A method for dividing continuous speech into syllables has been proposed. This method can be used for symmetrical stereo sound