photopsia 发表于 2025-3-26 22:51:46
http://reply.papertrans.cn/24/2341/234056/234056_31.pngmitten 发表于 2025-3-27 04:12:54
U-Net-Based Approach for Segmentation of Tables from Scanned Pages,odel is verified by testing the proposed system on the ICDAR 2013, ICDAR 2019, Marmot datasets and some randomly clicked images. Our model outperforms all the other methods presented in ICDAR 2019 table segmentation competition with an F score of 0.9694.craven 发表于 2025-3-27 08:13:01
,Camera Based Parking Slot Detection for Autonomous Parking,ikes, cones, carton boxes and trees, this method achieved a promising performance with F1 score higher than 97%. With the ability to run on low computational devices such as CPU, this method is adaptable to practical solutions for both AD and aftermarket ADAS systems.PATHY 发表于 2025-3-27 12:48:51
http://reply.papertrans.cn/24/2341/234056/234056_34.png洁净 发表于 2025-3-27 17:30:30
http://reply.papertrans.cn/24/2341/234056/234056_35.pngAtaxia 发表于 2025-3-27 20:09:33
http://reply.papertrans.cn/24/2341/234056/234056_36.png吃掉 发表于 2025-3-27 22:23:34
http://reply.papertrans.cn/24/2341/234056/234056_37.pngGorilla 发表于 2025-3-28 03:10:03
The Definitive Guide to MongoDBts on caricature recognition dataset and subsequent comparison of our proposed network against the baseline model quantitatively substantiates our hypothesis. While comparing the performance of our modified network against the baseline, we were able to improve the recognition accuracy by . for . setting and by . for . setting.Exterior 发表于 2025-3-28 09:41:37
Eelco Plugge,Peter Membrey,Tim Hawkinse maps in the final stage. Experimental results on the benchmark KITTI dataset show that the proposed modifications outperform the existing VoxelNet based models and other fusion based methods in terms of accuracy as well as time.Mobile 发表于 2025-3-28 12:38:40
Deep Learning-Based Smart Parking Management System and Business Model,nd updated automatically. Billing for the parking space usage will also be done automatically as per the regulated guidelines. Raspberry Pi and deep learning tools are used for the implementation. The proposed system is cost-effective and reduces time and energy.