• Current Research on Agriculture and Life Sciences
• /
• 제25권
• /
• pp.19-23
• /
• 2007

This research to streets at Daegu, and view adjective how people react when draw a bird into interior in city utilize and examine becoming direction of street trees system. Findings on survey, citizens appear by average 2.87 in management degree of street trees and average 2.76 in various of species of trees, discontent thing more or less by average 2.91 in satisfaction of the beauty, and demonstrate high shame from satisfaction for season stuff to comparative high 3.24, generally, citizens appear by there are parts to improve by do dissatisfaction about street trees system at Daegu. Also, street trees 2 lines plantings for a bird is average 3.05, shows affirmative feedback about that shrub utilization for a bird see high numerical value by average 3.4 and people draw a bird to space of city. Therefore, construct street trees friendly to the nature and be considered to need to re-establish set street trees environment in citizens preference to recover city ecosystem. As result that examine reaction which treat in street trees planting programs to draw birds into city through view adjective, Likert scales about 2 lines plantings of street trees and additional planting of shrub show affirmative feedback by average 3.4 both ordinary people and specialist group, if people want to draw birds into city by various of method, various research should be accomplished.

• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 2011년도 춘계학술대회
• /
• pp.152-156
• /
• 2011

AVM(Around View Monitoring) 시스템은 차량 주변에 대한 영상을 운전자에게 제공함으로써 안전 운전을 도와주는 시스템이다. 측면 거울이나 실내 거울을 통해서 볼 수 없는 사각 지대는 운전자의 안전 운행에 위협이 된다. 사각지대는 특히 후진 주행, 주차, 좁은 골목길 주행, 굽은 길 회전 등에서 차량 파손 및 인사 사고의 원인이 된다. 이러한 위험을 피하기 위해 후방의 장애물을 감지하는 후방 센서나 후방 카메라 등과 같은 운전자의 시야를 개선하는 ECU들이 개발되어 사용되고 있다. 이러한 ECU들의 도움을 받더라도 차량의 전, 후, 좌, 우 사방의 상황을 동시에 볼 수 없기 때문에 사고를 피하기 위해 운전자들은 주의를 기울여 운전해야 한다. 본 논문에서는 운전자의 안전 운행을 돕기 위해 차량 주변 영상을 실시간으로 제공하는 4SM 시스템을 설계하고 구현한다. 본 논문에서 제안하는 4SM 시스템은 차량의 전, 후, 좌, 우에 장착된 4대의 카메라로부터 입력된 영상을 통합하여 Bird's Eye View 영상을 운전자가 한 눈에 차량 주변 상황을 인식할 수 있도록 한다.

• Advances in Automotive Engineering
• /
• 제1권1호
• /
• pp.143-152
• /
• 2018

In this study an economical image stitching algorithm for use in automotive industry is developed for retrofittable panoramic image assistance applications. The aim of this project is to develop a driving assistance system known as Panoramic Parking Assistance (PPA) which is cheap, retrofittable and compatible for every type of automobiles. PPA generates bird's eye view image using cameras installed on the automobiles. Image stitching requires to get bird's eye view position of the vehicle. Panoramic images are wide area images that cannot be available by taking one shot, attained by stitching the overlapping areas. To achieve correct stitching many algorithms are used. This study includes some type of these algorithms and presents a simple one that is economical and practical. Firstly, the mathematical model of a wide view of angle camera is provided. Then distorted image correction is performed. Stitching is implemented by using the SIFT and SURF algorithms. It has been seen that using such algorithms requires complex image processing knowledge and implementation of high quality digital processors, which would be impracticle and costly for automobile use. Thus a simpler algorithm has been developed to decrase the complexity. The proposed algorithm uses one matching point for every couple of images and has ease of use and does not need high power processors. To show the efficiency, images coming from four distinct cameras are stitched by using the algorithm developed for the study and usability for automotive application is analyzed.

• 한국임상수의학회지
• /
• 제16권2호
• /
• pp.469-473
• /
• 1999

A 120 kg, 3-year-old, male ostrich (Struthio camelus) was submitted to the veterinary teaching hospital, Chonbuk national university, for chronic weight loss, anorexia, small hard fecal pellets, and lethargy. Illness was first noticed approximately 2 weeks before submission. Although the bird was able to stand, he was extremely weak and cachectic. Blood examination indicated that the bird was dehydration (PCV: 55%). Fluoroscopic and lateral abdominal radio-graphic view indicated impaction of distended proventriculus with bigger rocks than normal. Anesthesia was induced with ketamine (10 mg/kg, IV)-xylazine (1 mg/kg, IV) combination and maintained with ketamine (10 mg/kg, IV) at intervals of 8 to 15 minutes during the entire procedure. After induction of general anesthesia, the bird was positioned in dorsal recumbency. The skin was incised approximately 15cm caudal to the caudal margin of the sternum and extended 20 to 25 cm caudally. After the proventriculotomy was peformed, the impacted material that weighed 3kg contained rocks in 4 to 6cm diameter was removed. The incision of proventriculus was closed in 2 layers. Cephalothin sodium (30 mg/kg, IV, q12hrs) and dexamethazone (2 mg/kg, IM, q24hrs) was administered before and after 5 days of the operation. The bird was eating well and defecating normal feces after 3 weeks of the surgery

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
• /
• pp.216-216
• /
• 2000

In this paper, we introduce a new roadway-departure prevention algorithm and the developed Hardware-in-the-Loop-Simulator (HiLS) for applying the new algorithm. A sliding-mode controller is used for lateral position control. And, the HiLS consists of real car elements, a micro-control board, and a self-aligning torque generator Finally from the display module, the perspective view and bird view of the animated vehicle can be seen simultaneously.

• 한국동물학회:학술대회논문집
• /
• 한국동물학회 1999년도 한국생물과학협회 학술발표대회
• /
• pp.154.1-154
• /
• 1999

No Abstract, See Full Text

• International Journal of Automotive Technology
• /
• 제7권3호
• /
• pp.361-367
• /
• 2006

This paper describes a novel stereo vision based localization of free parking site, which recognizes the target position of automatic parking system. Pixel structure classification and feature based stereo matching extract the 3D information of parking site in real time. The pixel structure represents intensity configuration around a pixel and the feature based stereo matching uses step-by-step investigation strategy to reduce computational load. This paper considers only parking site divided by marking, which is generally drawn according to relevant standards. Parking site marking is separated by plane surface constraint and is transformed into bird's eye view, on which template matching is performed to determine the location of parking site. Obstacle depth map, which is generated from the disparity of adjacent vehicles, can be used as the guideline of template matching by limiting search range and orientation. Proposed method using both the obstacle depth map and the bird's eye view of parking site marking increases operation speed and robustness to visual noise by effectively limiting search range.

• 대한임베디드공학회논문지
• /
• 제17권1호
• /
• pp.41-49
• /
• 2022

This paper proposes a novel lane detection algorithm based on inverse perspective transformation and machine learning in lightweight embedded system. The inverse perspective transformation method is presented for obtaining a bird's-eye view of the scene from a perspective image to remove perspective effects. This method requires only the internal and external parameters of the camera without a homography matrix with 8 degrees of freedom (DoF) that maps the points in one image to the corresponding points in the other image. To improve the accuracy and speed of lane detection in complex road environments, machine learning algorithm that has passed the first classifier is used. Before using machine learning, we apply a meaningful first classifier to the lane detection to improve the detection speed. The first classifier is applied in the bird's-eye view image to determine lane regions. A lane region passed the first classifier is detected more accurately through machine learning. The system has been tested through the driving video of the vehicle in embedded system. The experimental results show that the proposed method works well in various road environments and meet the real-time requirements. As a result, its lane detection speed is about 3.85 times faster than edge-based lane detection, and its detection accuracy is better than edge-based lane detection.

• 한국정보기술학회 영문논문지
• /
• 제10권2호
• /
• pp.167-175
• /
• 2020

In this paper, we propose a traffic accident detection on vehicle-mounted camera. In the proposed method, the minimum bounding box coordinates the central coordinates on the bird's eye view and motion vectors of each vehicle object, and ego-motions of the vehicle equipped with dash-cam are extracted from the dash-cam video. By using extracted 4 kinds features as the input of Bi-LSTM (bidirectional LSTM), the accident probability (score) is predicted. To investigate the effect of each input feature on the probability of an accident, we analyze the performance of the detection the case of using a single feature input and the case of using a combination of features as input, respectively. And in these two cases, different detection models are defined and used. Bi-LSTM is used as a cascade, especially when a combination of the features is used as input. The proposed method shows 76.1% precision and 75.6% recall, which is superior to our previous work.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권8호
• /
• pp.1996-2015
• /
• 2023

High-definition (HD) maps can provide precise road information that enables an autonomous driving system to effectively navigate a vehicle. Recent research has focused on leveraging semantic segmentation to achieve automatic annotation of HD maps. However, the existing methods suffer from low recognition accuracy in automatic driving scenarios, leading to inefficient annotation processes. In this paper, we propose a novel semantic segmentation method for automatic HD map annotation. Our approach introduces a new encoder, known as the convolutional transformer hybrid encoder, to enhance the model's feature extraction capabilities. Additionally, we propose a multi-level fusion module that enables the model to aggregate different levels of detail and semantic information. Furthermore, we present a novel decoupled boundary joint decoder to improve the model's ability to handle the boundary between categories. To evaluate our method, we conducted experiments using the Bird's Eye View point cloud images dataset and Cityscapes dataset. Comparative analysis against stateof-the-art methods demonstrates that our model achieves the highest performance. Specifically, our model achieves an mIoU of 56.26%, surpassing the results of SegFormer with an mIoU of 1.47%. This innovative promises to significantly enhance the efficiency of HD map automatic annotation.