• 로봇학회논문지
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• 제14권3호
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• pp.170-178
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• 2019

This paper proposes a parking space detection method for autonomous parking by using the Around View Monitor (AVM) image and Light Detection and Ranging (LIDAR) sensor fusion. This method consists of removing obstacles except for the parking line, detecting the parking line, and template matching method to detect the parking space location information in the parking lot. In order to remove the obstacles, we correct and converge LIDAR information considering the distortion phenomenon in AVM image. Based on the assumption that the obstacles are removed, the line filter that reflects the thickness of the parking line and the improved radon transformation are applied to detect the parking line clearly. The parking space location information is detected by applying template matching with the modified parking space template and the detected parking lines are used to return location information of parking space. Finally, we propose a novel parking space detection system that returns relative distance and relative angle from the current vehicle to the parking space.

• 방송공학회논문지
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• 제27권7호
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• pp.999-1010
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• 2022

The increase in vehicle purchases worldwide is having a very significant impact on the availability of parking spaces. In particular, since it is difficult to secure a parking space in an urban area, it may be of great help to the driver to check vehicle parking information in advance. However, the current parking lot information is still operated semi-manually, such as notifications. Therefore, in this study, we propose a system for detecting a parking space using a relatively simple image processing method based on an image taken from the sky and evaluate its performance. The proposed method first converts the captured RGB image into a black-and-white binary image. This is to simplify the calculation for detection using discrete information. Next, a morphological operation is applied to increase the clarity of the binary image, and a template mask in the form of a bounding box indicating a parking space is applied to check the parking state. Twelve image samples and 2181 total of test, were used for the experiment, and a threshold of 40% was used to detect each parking space. The experimental results showed that information on the availability of parking spaces for parking users was provided with an accuracy of 95%. Although the number of experimental images is somewhat insufficient to address the generality of accuracy, it is possible to confirm the possibility of parking space detection with a simple image processing method.

• 한국멀티미디어학회논문지
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• 제19권2호
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• pp.418-427
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• 2016

A smart parking management requires to track a vehicle in a indoor parking lot and to detect the place where the vehicle is parked. An advanced parking system watches all space of the parking lot with CCTV cameras. We can use these cameras for vehicles tracking and detection. In order to cover a wide area with a camera, a fisheye lens is used. In this case the shape and size of an moving vehicle vary much with distance and angle to the camera. This makes vehicle detection and tracking difficult. In addition to the fisheye lens, the vehicle headlights also makes vehicle detection and tracking difficult. This paper describes a method of realtime vehicle detection and tracking robust to the harsh situation described above. In each image frame, we update the region of a vehicle and estimate the vehicle movement. First we approximate the shape of a car with a quadrangle and estimate the four sides of the car using multiple histograms of oriented gradient. Second we create a template by applying a distance transform to the car region and estimate the motion of the car with a template matching method.

• 한국통신학회논문지
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• 제36권10C호
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• pp.629-634
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• 2011

주차장에서 빈 공간을 분석하는 기술은 주차공간의 효율적인 사용이나 교통이 혼잡한 곳에서 유용하게 사용될 수 있다. 그러나 기존의 주차 공간 분석 방법은 실용적이지 못하거나 빠른 처리속도가 필요하다. 그래서 본 논문에서는 실시간 주차검출에 적합한 주차 모니터링 방법을 제안하고자 한다. 제안된 방법은 그레이레벨 영상을 이용하여 주차여부를 확인하고, 주차공간을 분석하는 방법을 사용하였다. 제안된 방법의 성능을 확인하기 위해 야외 주차장에서 129개의 동영상을 획득하여 실험한 결과 98.5%의 주차 공간 분석에 성공하여 제안된 방법이 주차 공간 분석에 효율적인 것을 입증하였다.

• 디지털산업정보학회논문지
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• 제18권4호
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• pp.31-40
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• 2022

YOLOv4 can be used for detecting parking vehicles in order to check a vehicle in out-door parking space. YOLOv4 has 9 anchor boxes in each of 13x13 grid cells for detecting a bounding box of object. Because anchor boxes are allocated based on each cell, there can be existed small observational error for detecting real objects due to the distance between neighboring cells. In this paper, we proposed YOLOv4 grid cell shift algorithm for improving the out-door parking vehicle detection accuracy. In order to get more chance for trying to object detection by reducing the errors between anchor boxes and real objects, grid cells over image can be shifted to vertical, horizontal or diagonal directions after YOLOv4 basic detection process. The experimental results show that a combined algorithm of a custom trained YOLOv4 and a cell shift algorithm has 96.6% detection accuracy compare to 94.6% of a custom trained YOLOv4 only for out door parking vehicle images.

• 한국컴퓨터정보학회논문지
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• 제27권8호
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• pp.9-21
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• 2022

이 논문에서 우리는 제4차 산업혁명의 핵심기술의 하나인 LiDAR 센서를 기반으로 기존 검지 카메라의 검지율을 개선하여 입출차 차량에 대해 100% 검지가능한 시스템을 개발하였다. 현재 운영 중인 주차장은 98% 정도의 차량번호 인식율에만 의존하고 있으므로 입출차 카운트의 불일치, 부정확한 정보 제공 등으로 사전 예약불가, 실시간 주차정보 불일치 등 여러 가지 문제를 안고 있다. 주차현황정보는 정확도 100% 수준으로 관리되어야 하며 이를 위해 우리는 LIDAR를 이용하여 주차장의 입출차 검지 체계를 구축하였다. 주로 자율주행 자동차의 차량 및 사물검지를 위해 필수적으로 사용되고 있는 LIDAR 센서를 응용하여 주차시스템을 개발하는 경우, 검지된 센싱 정보로 차량 입출차 정보의 정확성과 입출차 카운트의 신뢰도를 개선할 수 있다. LIDAR의 분해능은 100%로 보장이 되었고 주차장의 입차(+), 출차(-) 차량의 합계가 0이 되도록 구현할 수 있었다. 우리는 3,000대의 실제 주차장 출입 차량으로 테스트해 본 결과 주차 차량 입출차 정확도를 100%로 결과를 도출하였다.

• 스마트미디어저널
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• 제13권1호
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• pp.24-35
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• 2024

The combination of Artificial Intelligence and the Internet of Things (AIoT) has gained significant popularity. Deep neural networks (DNNs) have demonstrated remarkable success in various applications. However, deploying complex AI models on embedded boards can pose challenges due to computational limitations and model complexity. This paper presents an AIoT-based system for smart parking lots using edge devices. Our approach involves developing a detection model and a decision tree for occupancy status classification. Specifically, we utilize YOLOv5 for car license plate (LP) detection by verifying the position of the license plate within the parking space.

• 로봇학회논문지
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• 제10권1호
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• pp.33-41
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• 2015

Recent developments in robotics and intelligent vehicle area, bring interests of people in an autonomous driving ability and advanced driving assistance system. Especially fully automatic parking ability is one of the key issues of intelligent vehicles, and accurate parked vehicles detection is essential for this issue. In previous researches, many types of sensors are used for detecting vehicles, 2D LiDAR is popular since it offers accurate range information without preprocessing. The L shape feature is most popular 2D feature for vehicle detection, however it has an ambiguity on different objects such as building, bushes and this occurs misdetection problem. Therefore we propose the accurate vehicle detection method by using a 3D complete vehicle model in 3D point clouds acquired from front inclined 2D LiDAR. The proposed method is decomposed into two steps: vehicle candidate extraction, vehicle detection. By combination of L shape feature and point clouds segmentation, we extract the objects which are highly related to vehicles and apply 3D model to detect vehicles accurately. The method guarantees high detection performance and gives plentiful information for autonomous parking. To evaluate the method, we use various parking situation in complex urban scene data. Experimental results shows the qualitative and quantitative performance efficiently.

• 전자공학회논문지
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• 제52권3호
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• pp.147-153
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• 2015

본 논문에서는 주차장 환경에서의 차량 사고 감지방법을 제안한다. 제안하는 방법은 차량 검출, 차량 추적, 사고 감지의 3단계로 구성된다. 차량 검출 단계에서는 픽셀 기반의 전경 검출 방법과 모션맵을 이용하여 차량을 검출하고, 차량 추적 단계에서는 검출된 차량 정보를 바탕으로 차량의 이동을 추적한다. 마지막 단계인 차량 사고 감지 단계에서는 차량의 이동 방향에 맞추어 사고 감지 영역을 지정하고 사고 감지 영역에서 발생하는 움직임의 변화량을 분석하여 차량 사고를 감지한다. 실험을 통해 제안하는 방법은 주차장 환경에서 발생하는 차량 사고를 효과적으로 검출함을 보였다.

• 한국정보통신학회논문지
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• 제8권3호
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• pp.708-713
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• 2004

그레이 스케일 이미지를 이용한 차량 감지 방법은 주차장에서 빈 공간으로 진입하는 차량의 안내나 기타 유사한 목적들을 위해 제안되고 있다. 본 논문은 그레이레벨 이미지를 이용하여 주차여부를 확인하고, 주차공간을 분석하는 방법을 제안한다. 실험결과로써 각기 2일 동안 새벽부터 해질녘까지 야외 주차장을 이용하여 획득한 129개의 영상을 이용하여 실험한 결과 98.7％의 성공률을 나타내었다.