Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (12): 2898-2905.doi: 10.13229/j.cnki.jdxbgxb20220017
Previous Articles Next Articles
Xuan-jing SHEN1(),Tong-zhuang LIU1,Yu WANG1,Jia-wei LIU2()
CLC Number:
1 | Ichihashi H, Notsu A, Honda K, et al. Vacant parking space detector for outdoor parking lot by using surveillance camera and FCM classifier[C]∥2009 IEEE International Conference on Fuzzy Systems, Jeju, Korea, 2009: 127-134. |
2 | Tsai L W, Hsieh J W, Fan K C. Vehicle detection using normalized color and edge map[J]. IEEE transactions on Image Processing, 2007, 16(3): 850-864. |
3 | Huang C C, Tai Y S, Wang S J. Vacant parking space detection based on plane-based Bayesian hierarchical framework[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2013, 23(9): 1598-1610. |
4 | de Almeida P R L, Oliveira L S, Britto Jr A S, et al. PKLot——a robust dataset for parking lot classification[J]. Expert Systems with Applications, 2015, 42(11): 4937-4949. |
5 | 刘日, 李建国, 王小农. 立体车库车位分配建模与仿真[J]. 江苏大学学报:自然科学版, 2018, 39(1):19-25. |
Liu Ri, Li Jian-guo, Wang Xiao-nong. Modeling and simulation of parking space allocation in stereo garage[J]. Journal of Jiangsu University(Natural Science Edition), 2018, 39(1): 19-25. | |
6 | 于谦, 肖雄, 杨鸣鹏, 等. 基于车载排放测试驾驶行为对轻型汽油车排放的影响[J]. 江苏大学学报:自然科学版, 2022, 43(3):270-276. |
Yu Qian, Xiao Xiong, Yang Ming-peng,et al. Driving behavior impact on emissions of light-duty gasoline vehicle based on portable emission measurement system[J]. Journal of Jiangsu University(Natural Science Edition), 2022, 43(3):270-276. | |
7 | LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. |
8 | 申铉京, 沈哲, 黄永平, 等. 基于非局部操作的深度卷积神经网络车位占用检测算法[J]. 电子与信息学报, 2020, 42(9): 2269-2276. |
Shen Xuan-jing, Shen Zhe, Huang Yong-ping, et al. Deep convolutional neural network for parking space occupancy detection based on non-local operation[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2269-2276. | |
9 | Acharya D, Yan W, Khoshelham K. Real-time image-based parking occupancy detection using deep learning[C]∥Proceedings of the 5th Annual Research@Locate Conference, Adelaide, Australia, 2018: 33-40. |
10 | Amato G, Carrara F, Falchi F, et al. Deep learning for decentralized parking lot occupancy detection[J]. Expert Systems with Applications, 2017, 72: 327-334. |
11 | Amato G, Carrara F, Falchi F, et al. Car parking occupancy detection using smart camera networks and deep learning[C]∥2016 IEEE Symposium on Computers and Communication (ISCC), Messina, Italy, 2016: 1212-1217. |
12 | Ding Xiao-han, Zhang Xing-yu, Ma Ning-ning, et al. Repvgg: making VGG-style convnets great again[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 13733-13742. |
13 | 蔡英凤, 王海, 陈龙, 江浩斌. 采用视觉显著性和深度卷积网络的鲁棒视觉车辆识别算法[J]. 江苏大学学报:自然科学版, 2015, 36(3): 331-336. |
Cai Ying-feng, Wang hai, Chen Long, Jiang Hao-bin. Robust vehicle recognition algorithm using visual saliency and deep convolutional neural networks[J]. Journal of Jiangsu University(Natural Science Edition), 2015, 36(3): 331-336. | |
14 | Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]∥International Conference on Machine Learning, Lille, France, 2015: 448-456. |
15 | Ding Xiao-han, Guo Yu-chen, Ding Gui-gang, et al. Acnet: strengthening the kernel skeletons for powerful CNN via asymmetric convolution blocks[C]∥Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 2019: 1911-1920. |
16 | Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90. |
17 | Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[C]∥International Conference on Learning Representations, San Diego, CA, USA, 2015:1-14. |
18 | He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770-778. |
19 | Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 2818-2826. |
20 | Nurullayev S, Lee S W. Generalized parking occupancy analysis based on dilated convolutional neural network[J]. Sensors, 2019, 19(2): E277. |
[1] | Xian-yu QI,Wei WANG,Lin WANG,Yu-fei ZHAO,Yan-peng DONG. Semantic topological map building with object semantic grid map [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 569-575. |
[2] | Xiao-hu SHI,Jia-qi WU,Chun-guo WU,Shi CHENG,Xiao-hui WENG,Zhi-yong CHANG. Residual network based curve enhanced lane detection method [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 584-592. |
[3] | Peng GUO,Wen-chao ZHAO,Kun LEI. Dual⁃resource constrained flexible job shop optimal scheduling based on an improved Jaya algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 480-487. |
[4] | Jin-Zhen Liu,Guo-Hui Gao,Hui Xiong. Multi⁃scale attention network for brain tissue segmentation [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 576-583. |
[5] | Gui-he QIN,Jun-feng HUANG,Ming-hui SUN. Text input based on two⁃handed keyboard in virtual environment [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1881-1888. |
[6] | Xuan-jing SHEN,Xue-feng ZHANG,Yu WANG,Yu-bo JIN. Multi⁃focus image fusion algorithm based on pixel⁃level convolutional neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1857-1864. |
[7] | Fu-heng QU,Tian-yu DING,Yang LU,Yong YANG,Ya-ting HU. Fast image codeword search algorithm based on neighborhood similarity [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1865-1871. |
[8] | Tian BAI,Ming-wei XU,Si-ming LIU,Ji-an ZHANG,Zhe WANG. Dispute focus identification of pleading text based on deep neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1872-1880. |
[9] | Feng-feng ZHOU,Hai-yang ZHU. SEE: sense EEG⁃based emotion algorithm via three⁃step feature selection strategy [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1834-1841. |
[10] | Feng-feng ZHOU,Yi-chi ZHANG. Unsupervised feature engineering algorithm BioSAE based on sparse autoencoder [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1645-1656. |
[11] | Jun WANG,Yan-hui XU,Li LI. Data fusion privacy protection method with low energy consumption and integrity verification [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1657-1665. |
[12] | Ming-hua GAO,Can YANG. Traffic target detection method based on improved convolution neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1353-1361. |
[13] | Huai-jiang YANG,Er-shuai WANG,Yong-xin SUI,Feng YAN,Yue ZHOU. Simplified residual structure and fast deep residual networks [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1413-1421. |
[14] | Yao-long KANG,Li-lu FENG,Jing-an ZHANG,Fu CHEN. Outlier mining algorithm for high dimensional categorical data streams based on spectral clustering [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1422-1427. |
[15] | Wen-jun WANG,Yin-feng YU. Automatic completion algorithm for missing links in nowledge graph considering data sparsity [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1428-1433. |
|