Object-Based High-Rise Building Detection Using Morphological Building Index and Digital Map

High-rise buildings (HRBs) as modern and visually unique land use continue to increase due to urbanization. Therefore, large-scale monitoring of HRB is very important for urban planning and environmental protection. This paper performed object-based HRB detection using high-resolution satellite image and digital map. Three study areas were acquired from KOMPSAT-3A, KOMPSAT-3, and WorldView-3, and object-based HRB detection was performed using the direction according to relief displacement by satellite image. Object-based multiresolution segmentation images were generated, focusing on HRB in each satellite image, and then combined with pixel-based building detection results obtained from MBI through majority voting to derive object-based building detection results. After that, to remove objects misdetected by HRB, the direction between HRB in the polygon layer of the digital map HRB and the HRB in the object-based building detection result was calculated. It was confirmed that the direction between the two calculated using the centroid coordinates of each building object converged with the azimuth angle of the satellite image, and results outside the error range were removed from the object-based HRB results. The HRBs in satellite images were defined as reference data, and the performance of the results obtained through the proposed method was analyzed. In addition, to evaluate the efficiency of the proposed technique, it was confirmed that the proposed method provides relatively good performance compared to the results of object-based HRB detection using shadows.

A Decision Support System for Power Components Based on Improved YOLOv4-Tiny

The traditional image object detection algorithm applied in power inspection cannot effectively position power components, and the accuracy of recognition is low in scenes with some interference. In this research, we proposed a data-driven power detection method based on the improved YOLOv4-tiny model, which combined the ResNet-D module and the adjusted Res-CBAM to the backbone network of the existing YOLOv4-tiny module. We replaced the CSPOSANet module in the YOLOv4-tiny backbone network with the ResNet-D module to reduce the FLOPS required by the model. At the same time, the adjusted Res-CBAM whose feature fusion ways were replaced with stacking in the channels was combined as an auxiliary classifier. Finally, the features of five different receptive scales were used for prediction, and the display of the results was optimized by merging the prediction boxes. In the experiment, 57134 images collected on the power inspection line were processed and labeled, and the default anchor boxes were re-clustered, and the speed and accuracy of the model were evaluated by video and validation set of 3459 images. Processing multiple pictures and videos collected from the power inspection projects, we re-clustered the default anchor box and tested the speed and accuracy of the model. The results show that compared with the original YOLOv4-tiny model, the accuracy of our method that can position objects under occlusion and complex lighting conditions is guaranteed while the detection speed is about 13% faster.

Semi-Autonomous Learning Algorithm for Remote Image Object Detection Based on Aggregation Area Instance Refinement

Semi-autonomous learning for object detection has attracted more and more attention in recent years, which usually tends to find only one object instance with the highest score in each image. However, this strategy usually highlights the most representative part of the object instead of the whole object, which may lead to the loss of a lot of important information. To solve this problem, a novel end-to-end aggregate-guided semi-autonomous learning residual network is proposed to perform object detection. Firstly, a progressive modified residual network (MRN) is applied to the backbone network to make the detector more sensitive to the boundary features of the object. Then, an aggregate-based region-merging strategy (ARMS) is designed to select high-quality instances by selecting aggregation areas and merging these regions. The ARMS selects the aggregation areas that are highly related to the object through association coefficient, and then evaluates the aggregation areas through a similarity coefficient and fuses them to obtain high-quality object instance areas. Finally, a regression-locating branch is further developed to refine the location of the object, which can be optimized jointly with regional classification. Extensive experiments demonstrate that the proposed method is superior to state-of-the-art methods.

APPLICATION OF AUGMENTED REALITY TECHNOLOGY IN THE ANIMATION OF THE KANCIL CHILDREN'S STORYBOOK

There are relatively many Indonesian fairy tales that are spread in the community, have characters with good and evil temperaments. Usually take folk tales about teaching goodness, behaving smartly, and being able to distinguish between good and bad. Also teaches children not to be arrogant, insulting other people. The learning process is usually in the delivery of material using only pictures, dolls, or videos that are commonly seen by children. Conventional media used for learning reduce children's enthusiasm. On this occasion, to answer the problem of media that is less attractive to children by using Augmented Reality (AR), because it can help visualize abstract concepts so that it can be used for understanding the image object and the structure of an object model. results of making applications using Augmented Reality, assessed from the aspects of cognitive, affective, psychomotor, technological, and the benefits of getting good interpretation results.

Promotion of Purbalingga Tourism Object Using Augmented Reality Location Based Service & Virtual Reality 360° Based on Android

One of the technologies that can be used as an attraction in Purbalingga Regency is Augmented Reality (AR) merging virtual objects with real objects, where those objects can be seen in real-time. And using the Location-Based Service (LBS) method, this method is a service with the main parameters being the position and location of the user. LBS Location Based Service will be enhanced with Google Maps and GPS (Global Positioning System) to find its user position and information about several locations to be visited. Virtual Reality (VR) brings users can interact in a virtual world environment simulated by a computer, so that users feel they are inside. The appearance of the Image object using 3600 panoramic so that it can display images indefinitely and not cut and can look around the image. Application creation is done by Multimedia Development Life Cycle (MDLC) and Unity3D. The application has been successfully operated on the Android operating system with minimum specifications of 2GB RAM and KitKat version 4.4 OS. The results of the study have been conducted through a questionnaire to 15 respondents with 15 questions earned an The total value of the frequency of each question gets a score of 1,009 or a percentage of eligibility of 89,68% starting that the application is well worth using. Based on the black-box test, the application has been running properly and can display Location Based Service and 3600 panoramic and its information.