Parking Space Recognition Based on Deep Convolutional Neural Network

Chen, Zhuowen and Gao, Zijun and Li, Jiaqi and Zhang, Junjie and Dai, Yanan and Hu, Wenbo and Li, Changmao (2021) Parking Space Recognition Based on Deep Convolutional Neural Network. In: GREENETS 2021, 6-7 June 2021, Dalian, People’s Republic of China.

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Abstract

Automatic real-time recognition for lots of spots is a crucial and challenging task, which can significantly improve the intelligence level of a city and make it that much more convenient for people to travel. Deep learning models have been applied in various fields, and significant development has also been achieved. However, taking into account interference in recognition images and occlusions of lane lines, deep learning still lacks accuracy and real-time.Therefore, the paper argues that combining OpenCV processing and deep learning should be utilized. This method can improve recognition for identifying parking spaces and increase the intellectualization of a city. In this paper, a network model based on VGG19 has been proposed, and the SENet network module has been added to its output along with a performing secondary enhancement on training images. Advanced pre-processing to obtain accurate point coordinates has finally obtained a satisfactory effect. It also predicts images with severe interference and images with line occlusion. These two experimental results show that the network can effectively identify even with severe interference. At the same time, a large number of experimental evaluations also show that this method can also be applied to small target recognitions in many fields and can also be used as a basic product for small target detections and recognitions in the future.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: pretreatment deep learning point transformation senet network feature enhancement
Subjects: T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 10 Sep 2021 11:00
Last Modified: 10 Sep 2021 11:00
URI: https://eprints.eudl.eu/id/eprint/6784

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