3D AVM system for automotive applications

Su, Ching-Lung; Lee, Chung-Jan; Li, Meng-Sang; Chen, Kai-Ping

doi:10.1109/icics.2015.7459915

Cited by 11 publications

(3 citation statements)

References 9 publications

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“…Compared with other methods, the parking slot marking-based approach can be applied in wider situations, since it does not depend on the existence of adjacent vehicles or extra communication equipment. Moreover, as people pay more attention to vehicle safety, myriads of vehicles are equipped with the around view monitor (AVM) [18], which provides 360 • surveillance around the vehicle. Therefore, the vacant parking slot detection in the around view image can make full use of the existing equipment on the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Vacant Parking Slot Detection in the Around View Image Based on Deep Learning

Cao

Yan

et al. 2020

Sensors

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Due to the complex visual environment, such as lighting variations, shadows, and limitations of vision, the accuracy of vacant parking slot detection for the park assist system (PAS) with a standalone around view monitor (AVM) needs to be improved. To address this problem, we propose a vacant parking slot detection method based on deep learning, namely VPS-Net. VPS-Net converts the vacant parking slot detection into a two-step problem, including parking slot detection and occupancy classification. In the parking slot detection stage, we propose a parking slot detection method based on YOLOv3, which combines the classification of the parking slot with the localization of marking points so that various parking slots can be directly inferred using geometric cues. In the occupancy classification stage, we design a customized network whose size of convolution kernel and number of layers are adjusted according to the characteristics of the parking slot. Experiments show that VPS-Net can detect various vacant parking slots with a precision rate of 99.63% and a recall rate of 99.31% in the ps2.0 dataset, and has a satisfying generalizability in the PSV dataset. By introducing a multi-object detection network and a classification network, VPS-Net can detect various vacant parking slots robustly.

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Section: Introductionmentioning

confidence: 99%

Vacant Parking Slot Detection in the Around View Image Based on Deep Learning

Cao

Yan

et al. 2020

Sensors

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“…Currently, most vehicles are equipped with Around View Monitors (AVM [1]). Parking slot detection using AVM is easily affected by complex visual environments such as light occlusion, intensity variation and visual accuracy.…”

mentioning

confidence: 99%

An efficient vision-based parking slot detection method

Song

Liu

Tang

et al. 2023

Preprint

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High-accuracy detection of vacant parking slots using visual system is a prerequisite for automatic parking, which is significant for the promotion of automatic parking. Complex visual environment such as light occlusion, intensity variations and visual accuracy raises significant challenges for vision-based system parking slot detection. To address such problems, REPSDet (Resnet50 based Parking Slot Detection) is proposed for parking slot detection and occupancy classification in this paper, which is based on deep learning. Parking slot proximity information could be fully extracted by REPSDet,which is combined with parking slot classification and location information to improve the parking slot detection effect. For parking slot detection, REPS-Net is proposed in this paper, which is an improved yolov4 parking slot detection method. In REPS-Net, marker point positioning combined with geometric clues are used to infer the precise location of parking slots. For occupancy classification, the deep convolutional network m\_DCNN is proposed in this paper. Network structure and operation performance are optimized by m_DCNN, and the accuracy of parking slot detection is further improved. Experimental results show that REPSDet achieves precision rate of 97.77% and recall rate of 99.38% on the PS2.0_A dataset, which is significantly better than compared methods.

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“…The statistics results of the national traffic accidents show that the proportion of accidents caused by drivers' vision limitation, the reflection of delay, judgment error, and improper operation accounted for up to 40% of the total accidents [3][4][5]. In order to solve the above problem, the advanced driving assistance system (ADAS) received more and more attention, such as lane departure warning system (LDWS), pro-collision warning system (FCWS), blind spot monitoring (BSD), and around view monitor (AVM) [6,7]. Among them, AVM is used to provide the driver with the 360-degree video image information around the body, in the parking, and in crowded city traffic conditions to reduce the user's visual blind area, to help users to better judge the road traffic conditions around the vehicle, to avoid collision with pedestrians and vehicles around, and to make the driving process safer and more convenient [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Ring Fusion of Fisheye Images Based on Corner Detection Algorithm for Around View Monitoring System of Intelligent Driving

Zhao

Gao

Zhang

et al. 2018

Journal of Robotics

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In order to improve the visual effect of the around view monitor (AVM), we propose a novel ring fusion method to reduce the brightness difference among fisheye images and achieve a smooth transition around stitching seam. Firstly, an integrated corner detection is proposed to automatically detect corner points for image registration. Then, we use equalization processing to reduce the brightness among images. And we match the color of images according to the ring fusion method. Finally, we use distance weight to blend images around stitching seam. Through this algorithm, we have made a Matlab toolbox for image blending. 100% of the required corner is accurately and fully automatically detected. The transition around the stitching seam is very smooth, with no obvious stitching trace.

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3D AVM system for automotive applications

Cited by 11 publications

References 9 publications

Vacant Parking Slot Detection in the Around View Image Based on Deep Learning

Vacant Parking Slot Detection in the Around View Image Based on Deep Learning

An efficient vision-based parking slot detection method

Ring Fusion of Fisheye Images Based on Corner Detection Algorithm for Around View Monitoring System of Intelligent Driving

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