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Author

Jun Qiu

Bio: Jun Qiu is an academic researcher from East China University of Science and Technology. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Papers
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Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed a multi-clue recovery model to reconstruct parking spots, which can reach more than 80% accuracy in most test cases, compared with several existing algorithms, and the experimental result shows that it has a higher accuracy than others.
Abstract: Due to various complex environmental factors and parking scenes, there are more stringent requirements for automatic parking than the manual one. The existing auto-parking technology is based on space or plane dimension, where the former usually ignores the ground parking spot lines which may cause parking at a wrong position, while the latter often costs a lot of time in object classification which may decreases the algorithm applicability. In this paper, we propose a Generative Parking Spot Detection algorithm which uses a multi-clue recovery model to reconstruct parking spots. In the proposed method, we firstly dismantle the parking spot geometrically for marking the location of its corresponding corners and then use a micro-target recognition network to find corners from the ground image taken by car cameras. After these, we use the multi-clue model to correct the fully pairing map so that the reliable true parking spot can be recovered correctly. The proposed algorithm is compared with several existing algorithms, and the experimental result shows that it has a higher accuracy than others which can reach more than 80% in most test cases.

3 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, a strategy for generating the shortest parking path based on a bidirectional breadth-first search algorithm combined with a modified Bellman-Ford algorithm is proposed for automatic parking systems.
Abstract: The development of the automobile industry and the increase in car ownership has brought great traffic pressures to the city, among which, the difficulty of parking has become a serious problem to the majority of drivers. An automatic parking system can help drivers to complete parking operation or automatic parking task, and a decision control system is an important part of automatic parking system. In this paper, a strategy for generating the shortest parking path based on a bidirectional breadth-first search algorithm combined with a modified Bellman–Ford algorithm is proposed for automatic parking systems. Experimental results show that this scheme can improve the performance of an automatic parking system, especially in a complex environment.

3 citations

Book ChapterDOI
TL;DR: Wang et al. as mentioned in this paper proposed a power line detection method based on feature fusion deep learning network, which makes full use of the fusion features, which is combined with the inherent features and auxiliary information of aerial power line images.
Abstract: AbstractNowadays, the network of transmission lines is gradually spreading all over the world. With the popularization of UAV and helicopter applications, it is of great significance for low-altitude safety aircraft to detect power lines in advance and implement obstacle avoidance. The Power Line Detection (PLD) in a complex background environment is particularly important. In order to solve the problem of false detection of power lines caused by complex background images, a PLD method based on feature fusion deep learning network is proposed in this paper. Firstly, in view of the problems of low accuracy and poor generalization by using the traditional PLD in complex background environments, a rough extraction module that makes full use of the fusion features is constructed, which is combined with the inherent features and auxiliary information of aerial power line images. Secondly, an output fusion module is constructed, the weights of which are actively learned in the network training session. Finally, the fusion module fuses the decisions of different depths for output. The experimental results show that the proposed method can effectively improve the accuracy of power line detection.KeywordsDeep learningFeature fusionPower Line DetectionAuxiliary information
Journal ArticleDOI
TL;DR: Experimental results show that the improved “feature bag” and “spatial pyramid matching” algorithms on the basis of 3D feature extraction algorithm can effectively utilize the spatial information of three-dimensional images and achieve satisfactory results in the classification and recognition of human magnetic resonance images.
Abstract: Image classification and recognition has a very wide range of applications in computer vision, which involves many fields, such as image retrieval, image analysis, and robot positioning. Especially with the rise of brain science and cognitive science research, as well as the increasing diversification of imaging means, three-dimensional image data mainly based on magnetic resonance image plays an increasingly important role in image classification and recognition, especially in medical image classification and recognition. However, due to the high dimensional characteristics of human magnetic resonance images, human readability is reduced. Therefore, classification and recognition of 3-dimensional images is still a challenge. In order to better extract local features from images and effectively use their spatial information, this paper improved the “feature bag” and “spatial pyramid matching” algorithms on the basis of 3D feature extraction algorithm and proposed an image classification framework based on 3D feature extraction algorithm. Firstly, the multiresolution “3D spatial pyramid” algorithm, the multiscale image segmentation and image representation method, and the SVM classifier and feature fusion method are described. Secondly, the gender information contained in the magnetic resonance images is classified and recognized on the three databases selected in the experiment. Experimental results show that this method can effectively utilize the spatial information of three-dimensional images and achieve satisfactory results in the classification and recognition of human magnetic resonance images.