Uncontrolled garbage disposal related to urban settings can be extremely harmful to the health of individuals as well as the environment. This paper proposes the construction of an intelligent surveillance system based on the YOLOv5 object detection model and an ultra-convolutional net (U-Net), which we will call (YOLOv5-U-Net model), capable of monitoring waste management facilities in real-time through image processing and artificial intelligence. An illustration of an intelligent surveillance system is provided in the following statement. Besides identifying different categories of garbage and possible risks to public health, the system can also identify situations of unsuitable accumulation of waste. For that purpose, to ensure that local authorities will act in due time, the framework integrates several technologies, including object detection algorithms, classification networks as well as real-time warning systems. It is through the amalgamation of these technologies. Testing of the prototype has elicited an outcome; that accuracy related to waste categorization has increased, while reaction times have decreased, all discovered due to prototype examination. Implementation of this strategy does not only increase the linkage between environmental monitoring as well as protection of public health but also gives some help in promoting a conscious urban development, right through ensuring health.
The paper concentrates on the latest developments in the field of deep-learning-based image deblurring and specifically, Convolutional Neural Networks (CNNs) and how they are able to be used to deblur images. It discusses the different forms of blur such as motion blur, out-of-focus blur, and mixed blur and compares these methods under the basis of blind and non-blind methods. The article sheds light on the various architecture and model design, loss functions, and performance indicators applied in image deblurring. Moreover, it draws attention to the issues that are presently observed in the sphere and gives possible path directions of the future research. The review has condensed and synthesized existing literature to provide a clear overview of the current solutions in image deblurring and offers guidance to the researchers on how to come up with the more precise, efficient, and adaptive methods of deblurring. The developments are meant to enhance the use of image restoration techniques in practical applications and this will lead to the quality and reliability of deblurring processes.
Initial delineation of prospecting zones of groundwater was conducted in the present studyusing remote sensing and geographic information system (GIS) techniques. It has been preparingan integrated geographic database of spatial and non-spatial data for the study area. The spatialdata were generated by using image processing software (Erdas 8.3) and GIS software (Arc view3.3) enhanced by real frequent field visits of the study area. These data include: surface featureswhich give a direct and indirect indicators of the existence of groundwater and affect to thegroundwater movement such as hydrogeomorphological, drainage density, slope, landuse andsoil maps. The non spatial data were derived primarily from real views during field visits to thestudy area and from the existing writing or previous studies. All the data generated were saved inthe GIS databank for the purpose of digitization, computational and generate the best possibleoutput results to determine the extent of possible areas where the water that exists for the purposeof prospecting. Results showed that more areas could be have very good categories of prospectzones are the southern parts of the study area, which covers about 375 Km2 while the northernareas, which covers about 164 Km2 of the study area are grouped as runoff zone. Accordingly thepossibilities of the presence of groundwater are poor to negligible in this zone. The current studydemonstrated that a remote sensing and GIS technique are very effective tools that can give theinitial predictions on the presence or probability of the presence of ground water in areas whichhave the same considered geological deposits for the study area.
This research represents part of the current attempts to employ remote sensing data in the scopes of the civil engineering and the geotechnical engineering applications. There is great need to know the kinds of soil and their geotechnical properties, to create recent maps which have the capability and high flexibility to deal with them in digitizing way. Therefore GIS techniques are employed in the soil of area of study . By using ArcView software, a geographical database and information about soil chemical properties analysis have been registered and constructed digitally to represent the geotechnical soil characteristics maps . The work includes the digital image processing ( digital classification techniques) by using ERDAS, ver.,8.4 package, and classify the soil of study area by using the supervise and unsupervised techniques . The geotechnical maps by using GIS techniques depend on remote sensing data are the better to represent the ground truth regarding the characteristics of soil , in comparison with the traditional method, because they are easy way to produce, use, store and update, in addition they save in efforts, time and cost . The results of this study have shown that the soil of study area is gypsum where it ratio exceeded the allowable ratio ( 10.75 % ) for all samples . In addition the total Soluble Salts ratio and SO4 ratio high compared to allowable ratio (10 % , 5 %) respectively .
The field of image processing has several applications in our daily life. The image quality can be affected by a wide variety of deformations during image acquisition, transmission, compression, etc. Image compression is one of the applications where the quality of the image plays an important role since it can be used to evaluate the performance of various image compression techniques. Many image quality assessment metrics have been proposed. This paper proposes a new metric to assess the quality of compressed images. The principle idea of this metric is to estimate the amount of lost information during image compression process using three components: error magnitude, error location and error distribution. We denote this metric as MLD, which combines the objective assessment (error magnitude) and the subjective assessment (error location and error distribution). First, the metric is used to estimate the quality of compressed images using the JPEG algorithm as this is a standard lossy image compression technique. Then, the metric is used to estimate the quality of compressed images using other compression techniques. The results illustrate that the proposed quality metric is correlated with the subjective assessment better than other well-known objective quality metrics such as SSIM, MSE and PSNR. Moreover, using the proposed metric the JPEG2000 algorithm produces better quality results as compared to the JPEG algorithm especially for higher compression ratios
A fundamental score of this paper is to explain in detail how to create a 3D-provided modeled scene by data obtained at minimal cost to the client or users by manufacturing a smart, automated system for heritage documentation (SAS-HD). The steps can be classified by manufacturing, parts connection and simulation, selection of work sites, and obtaining data. The most important acquiesced data are digital images which are fundamentally used by the structure from motion (SFM) approach in MATLAB. The obtained images were subjected to sequenced tips by getting 3D sparse points of each object. Two objects have been considered by this article in an indoor case study: first feature is Ishtar Gate, and the second one is the winged ball inside Iraqi museum in Baghdad capital. The results are promising; hence 3- Structure From Motion SFM method has been utilized to document heritage by manipulating 3D models on MATLAB interphase, which is approved for its efficiency as well as its quick, super advanced processing steps.