{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T04:31:48Z","timestamp":1727065908227},"reference-count":45,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,13]],"date-time":"2023-08-13T00:00:00Z","timestamp":1691884800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jilin Provincial Science and Technology Innovation Center for Network Database Application","award":["YDZJ202302CXJD027"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Traffic sign detection is a crucial task in computer vision, finding wide-ranging applications in intelligent transportation systems, autonomous driving, and traffic safety. However, due to the complexity and variability of traffic environments and the small size of traffic signs, detecting small traffic signs in real-world scenes remains a challenging problem. In order to improve the recognition of road traffic signs, this paper proposes a small object detection algorithm for traffic signs based on the improved YOLOv7. First, the small target detection layer in the neck region was added to augment the detection capability for small traffic sign targets. Simultaneously, the integration of self-attention and convolutional mix modules (ACmix) was applied to the newly added small target detection layer, enabling the capture of additional feature information through the convolutional and self-attention channels within ACmix. Furthermore, the feature extraction capability of the convolution modules was enhanced by replacing the regular convolution modules in the neck layer with omni-dimensional dynamic convolution (ODConv). To further enhance the accuracy of small target detection, the normalized Gaussian Wasserstein distance (NWD) metric was introduced to mitigate the sensitivity to minor positional deviations of small objects. The experimental results on the challenging public dataset TT100K demonstrate that the SANO-YOLOv7 algorithm achieved an 88.7% mAP@0.5, outperforming the baseline model YOLOv7 by 5.3%.<\/jats:p>","DOI":"10.3390\/s23167145","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T15:07:10Z","timestamp":1692025630000},"page":"7145","source":"Crossref","is-referenced-by-count":18,"title":["A Small Object Detection Algorithm for Traffic Signs Based on Improved YOLOv7"],"prefix":"10.3390","volume":"23","author":[{"given":"Songjiang","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China"}]},{"given":"Shilong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China"}]},{"given":"Peng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China"},{"name":"Chongqing Research Institute, Changchun University of Science and Technology, Chongqing 401120, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"You, L., Ke, Y., Wang, H., You, W., Wu, B., and Song, X. (2019, January 25\u201330). Small Traffic Sign Detection and Recognition in High-Resolution Images. Proceedings of the Cognitive Computing\u2014ICCC 2019 Proceedings of the: Third International Conference, Held as Part of the Services Conference Federation, SCF 2019, San Diego, CA, USA.","DOI":"10.1007\/978-3-030-23407-2_4"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"39185","DOI":"10.1109\/ACCESS.2023.3266825","article-title":"A Traffic Sign Recognition Method Under Complex Illumination Conditions","volume":"11","author":"Yan","year":"2023","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"38931","DOI":"10.1109\/ACCESS.2020.2975828","article-title":"Multi-Feature Fusion and Enhancement Single Shot Detector for Traffic Sign Recognition","volume":"8","author":"Jin","year":"2020","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., and Hu, S. (2016, January 27\u201330). Traffic-sign detection and classification in the wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.232"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Akatsuka, H., and Imai, S. (1987). Road Signposts Recognition System, SAE International. SAE Technical Paper.","DOI":"10.4271\/870239"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Wu, F. (2014, January 17\u201319). Real-time traffic sign detection via color probability model and integral channel features. Proceedings of the Pattern Recognition: 6th Chinese Conference, CCPR 2014, Changsha, China.","DOI":"10.1007\/978-3-662-45643-9_58"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kiran, C.G., Prabhu, L.V., Rahiman, V.A., Rajeev, K., and Sreekumar, A. (2008, January 19\u201321). Support vector machine learning based traffic sign detection and shape classification using distance to borders and distance from center features. Proceedings of the TENCON 2008\u20132008 IEEE Region 10 Conference, Hyderabad, India.","DOI":"10.1109\/TENCON.2008.4766535"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Garrido, M.\u00c1., Sotelo, M.\u00c1., and Mart\u00edn-Gorostiza, E. (2005, January 7\u201311). Fast road sign detection using hough transform for assisted driving of road vehicles. Proceedings of the Computer Aided Systems Theory\u2014EUROCAST 2005: 10th International Conference on Computer Aided Systems Theory, Las Palmas de Gran Canaria, Spain.","DOI":"10.1007\/11556985_71"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1721","DOI":"10.1007\/s00138-013-0540-y","article-title":"Triangular traffic signs detection based on RSLD algorithm","volume":"24","author":"Boumediene","year":"2013","journal-title":"Mach. Vis. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Y\u0131ld\u0131z, G., and Dizdaro\u011flu, B. (2019, January 6\u20137). Traffic sign detection via color and shape-based approach. Proceedings of the 2019 1st International Informatics and Software Engineering Conference (UBMYK), Ankara, Turkey.","DOI":"10.1109\/UBMYK48245.2019.8965590"},{"key":"ref_11","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE computer society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ardianto, S., Chen, C.J., and Hang, H.M. (2017, January 22\u201324). Real-time traffic sign recognition using color segmentation and SVM. Proceedings of the 2017 International Conference on Systems, Signals and Image Processing (IWSSIP), Poznan, Poland.","DOI":"10.1109\/IWSSIP.2017.7965570"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.1109\/TVT.2015.2500275","article-title":"Accurate and efficient traffic sign detection using discriminative adaboost and support vector regression","volume":"65","author":"Chen","year":"2015","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201324). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_16","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst., 28."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"29742","DOI":"10.1109\/ACCESS.2020.2972338","article-title":"A cascaded R-CNN with multiscale attention and imbalanced samples for traffic sign detection","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3579","DOI":"10.1109\/ACCESS.2020.3047414","article-title":"Traffic sign detection and recognition using multi-scale fusion and prime sample attention","volume":"9","author":"Cao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shao, F., Wang, X., Meng, F., Zhu, J., Wang, D., and Dai, J. (2019). Improved faster R-CNN traffic sign detection based on a second region of interest and highly possible regions proposal network. Sensors, 19.","DOI":"10.3390\/s19102288"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollar, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mohd-Isa, W.N., Abdullah, M.S., Sarzil, M., Abdullah, J., Ali, A., and Hashim, N. (2020, January 26\u201327). Detection of Malaysian traffic signs via modified YOLOv3 algorithm. Proceedings of the 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), Sakheer, Bahrain.","DOI":"10.1109\/ICDABI51230.2020.9325690"},{"key":"ref_25","first-page":"2472","article-title":"Improved traffic sign recognition algorithm based on YOLO v3 algorithm","volume":"40","author":"Jiang","year":"2020","journal-title":"J. Comput. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7853","DOI":"10.1007\/s00521-022-08077-5","article-title":"Improved YOLOv5 network for real-time multi-scale traffic sign detection","volume":"35","author":"Wang","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"116783","DOI":"10.1016\/j.image.2022.116783","article-title":"Traffic sign detection algorithm based on improved YOLOv4-Tiny","volume":"107","author":"Yao","year":"2022","journal-title":"Signal Process. Image Commun."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., and Yan, S. (2017, January 21\u201326). Perceptual generative adversarial networks for small object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.211"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3456","DOI":"10.1109\/TCSVT.2020.3038649","article-title":"Efficient selective context network for accurate object detection","volume":"31","author":"Nie","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1968","DOI":"10.1109\/TMM.2021.3074273","article-title":"Extended feature pyramid network for small object detection","volume":"24","author":"Deng","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 18\u201322). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_33","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_34","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_35","unstructured":"Wang, C.Y., Yeh, I.H., and Liao, H.Y.M. (2021). You only learn one representation: Unified network for multiple tasks. arXiv."},{"key":"ref_36","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020, January 23\u201328). End-to-end object detection with transformers. Proceedings of the European Conference on Computer Vision, Glasglow, UK.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_38","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2020). Deformable detr: Deformable transformers for end-to-end object detection. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Zhang, H., and Lu, X. (2022). Adaptive Feature Fusion for Small Object Detection. Appl. Sci., 12.","DOI":"10.3390\/app122211854"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gao, T., Wushouer, M., and Tuerhong, G. (2023). DMS-YOLOv5: A Decoupled Multi-Scale YOLOv5 Method for Small Object Detection. Appl. Sci., 13.","DOI":"10.3390\/app13106124"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Pan, X., Ge, C., Lu, R., Song, S., Chen, G., Huang, Z., and Huang, G. (2022, January 18\u201324). On the integration of self-attention and convolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00089"},{"key":"ref_42","unstructured":"Li, C., Zhou, A., and Yao, A. (2022). Omni-dimensional dynamic convolution. arXiv."},{"key":"ref_43","unstructured":"Wang, J., Xu, C., Yang, W., and Yu, L. (2021). A normalized Gaussian Wasserstein distance for tiny object detection. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Cheng, P., Liu, W., Zhang, Y., and Ma, H. (2018, January 5\u20137). LOCO: Local context based faster R-CNN for small traffic sign detection. Proceedings of the MultiMedia Modeling: 24th International Conference, MMM 2018, Bangkok, Thailand.","DOI":"10.1007\/978-3-319-73603-7_27"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"106838","DOI":"10.1109\/ACCESS.2019.2932731","article-title":"An Improved Faster R-CNN for Small Object Detection","volume":"7","author":"Cao","year":"2019","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7145\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T00:54:48Z","timestamp":1702947288000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7145"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,13]]},"references-count":45,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23167145"],"URL":"https:\/\/doi.org\/10.3390\/s23167145","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,13]]}}}