{"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/m3e-yolo-a-new-lightweight-network-for","title":"M3E-Yolo: A New Lightweight Network for Traffic Sign Recognition","arxiv_id":null,"date":"2023-01-19","proceeding":"19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) 2023 1","authors":["Guo Haoran","Li Fan","Kuang Ping","Xiong Gang"],"abstract":"Traffic sign recognition is committed to ensuring the safety of automatic driving. Inspired by YOLOv5, this paper proposes a new model to solve the problem of poor balance between the accuracy and efficiency of existing algorithms in traffic sign recognition. Firstly, the lightweight network MobileNetV3 is introduced for feature extraction to reduce the number of parameters. Secondly, attention mechanism module is introduced to enhance channel features, which makes up for the reduced accuracy caused by the simplified model. Experiments show that the mAP value trained by our model on the Chinese traffic sign dataset reaches 93.6%, which is similar to the level of YOLOv5, and the number of parameters is less than a quarter of YOLOv5.","url_abs":"https://ieeexplore.ieee.org/abstract/document/10016618","url_pdf":"https://ieeexplore.ieee.org/abstract/document/10016618","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":"traffic-sign-detection","task_name":"Traffic Sign Detection"},{"task_slug":"traffic-sign-recognition","task_name":"Traffic Sign Recognition"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"depthwise-convolution","method_name":"Depthwise Convolution"},{"method_slug":"depthwise-separable-convolution","method_name":"Depthwise Separable Convolution"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"hard-swish","method_name":"Hard Swish"},{"method_slug":"inverted-residual-block","method_name":"Inverted Residual Block"},{"method_slug":"pointwise-convolution","method_name":"Pointwise Convolution"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"relu6","method_name":"ReLU6"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"squeeze-and-excitation-block","method_name":"Squeeze-and-Excitation Block"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/traffic-sign-detection-on-cctsdb2021","task":"Traffic Sign Detection","dataset":"CCTSDB2021","model":"M3E-Yolo","rank_in_archive_order":2,"of":2,"metrics":{"mAP@0.5":"93.4"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}