{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/t-yolo-tiny-vehicle-detection-based-on-yolo","title":"T-YOLO: Tiny Vehicle Detection Based on YOLO and Multi-Scale Convolutional Neural Networks","arxiv_id":null,"date":"2021-12-01","proceeding":"IEEE Access 2021 12","authors":["Daniel Padilla Carrasco","Hatem RashwanHatem Rashwan","Miguel Ángel García","Domenec Puig"],"abstract":"To solve real-life problems for different smart city applications, using deep Neural Network, such as parking occupancy detection, requires fine-tuning of these networks. For large parking, it is desirable to use a cenital-plane camera located at a high distance that allows the monitoring of the entire parking space or a large parking area with only one camera. Today’s most popular object detection models, such as YOLO, achieve good precision scores at real-time speed. However, if we use our own data different from that of the general-purpose datasets, such as COCO and ImageNet, we have a large margin for improvisation. In this paper, we propose a modified, yet lightweight, deep object detection model based on the YOLO-v5 architecture. The proposed model can detect large, small, and tiny objects. Specifically, we propose the use of a multi-scale mechanism to learn deep discriminative feature representations at different scales and automatically determine the most suitable scales for detecting objects in a scene (i.e., in our case vehicles). The proposed multi-scale module reduces the number of trainable parameters compared to the original YOLO-v5 architecture. The experimental results also demonstrate that precision is improved by a large margin. In fact, as shown in the experiments, the results show a small reduction from 7.28 million parameters of the YOLO-v5-S profile to 7.26 million parameters in our model. In addition, we reduced the detection speed by inferring 30 fps compared to the YOLO-v5-L/X profiles. In addition, the tiny vehicle detection performance was significantly improved by 33% compared to the YOLO-v5-X profile.","url_abs":"https://ieeexplore.ieee.org/document/9658533","url_pdf":"https://www.researchgate.net/publication/357258399_T-YOLO_Tiny_vehicle_detection_based_on_YOLO_and_multi-scale_convolutional_neural_networks/fulltext/61c3c877c48a3d26b74a7594/T-YOLO-Tiny-vehicle-detection-based-on-YOLO-and-multi-scale-convolutional-neural-networks.pdf?origin=publicationDetail&_sg%5B0%5D=cnnzAJDqU61aGdwfG6pFIoDItYCW7wHZ-sqVDeZx8G0-fZQmI1TSWEH31WCi93ShBilfyufWLSwBV-0i9-GFKQ.IPLAdUZnICAwnHhzl_bEi_ZFm_zAUTEJzIgsJ-zZdXNGJTY7Nc015oj5AFsJgbzoUD4X3Y-AIvFAXyEo0-SBzA&_sg%5B1%5D=nNwZJXww5diSEIC64lZ0mVnmXZscPJLB5NEujX0EXNbrV7Z9rYMu38Zm5xHWY6ODcsbexCa2xAj9zNEwJyq4PEVO3Xdbb_l5AIM8HfAdKQg9.IPLAdUZnICAwnHhzl_bEi_ZFm_zAUTEJzIgsJ-zZdXNGJTY7Nc015oj5AFsJgbzoUD4X3Y-AIvFAXyEo0-SBzA&_iepl=&_rtd=eyJjb250ZW50SW50ZW50IjoibWFpbkl0ZW0ifQ%3D%3D","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"parking-space-occupancy","task_name":"Parking Space Occupancy"},{"task_slug":"object-detection-1","task_name":"object-detection"},{"task_slug":"vehicle-detection","task_name":"vehicle detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/parking-space-occupancy-on-pklot","task":"Parking Space Occupancy","dataset":"PKLot","model":"T-YOLO","rank_in_archive_order":1,"of":3,"metrics":{"Average-mAP":"0.9985"},"uses_additional_data":true}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}