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However, the accurate detection of traffic signs remains challenging, especially under extreme conditions. This paper proposes a novel model called Traffic Sign Yolo (TS\u2010Yolo) based on the convolutional neural network to improve the detection and recognition accuracy of traffic signs, especially under low visibility and extremely restricted vision conditions. A copy\u2010and\u2010paste data augmentation method was used to build a large number of new samples based on existing traffic\u2010sign datasets. Based on You Only Look Once (YoloV5), the mixed depth\u2010wise convolution (MixConv) was employed to mix different kernel sizes in a single convolution operation, so that different patterns with various resolutions can be captured. Furthermore, the attentional feature fusion (AFF) module was integrated to fuse the features based on attention from same\u2010layer to cross\u2010layer scenarios, including short and long skip connections, and even performing the initial fusion with itself. The experimental results demonstrated that, using the YoloV5 dataset with augmentation, the precision was 71.92, which was increased by 34.56 compared with the data without augmentation, and the mean average precision mAP_0.5 was 80.05, which was increased by 33.11 compared with the data without augmentation. When MixConv and AFF were applied to the TS\u2010Yolo model, the precision was 74.53 and 2.61 higher than that with data augmentation only, and the value of mAP_0.5 was 83.73 and 3.68 higher than that based on the YoloV5 dataset with augmentation only. Overall, the performance of the proposed method was competitive with the latest traffic sign detection approaches.<\/jats:p>","DOI":"10.1155\/2021\/9984787","type":"journal-article","created":{"date-parts":[[2021,7,10]],"date-time":"2021-07-10T19:20:08Z","timestamp":1625944808000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["A Novel Neural Network Model for Traffic Sign Detection and Recognition under Extreme Conditions"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9613-2889","authenticated-orcid":false,"given":"Haifeng","family":"Wan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4272-9417","authenticated-orcid":false,"given":"Lei","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manman","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinglong","family":"You","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Qu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8451-4572","authenticated-orcid":false,"given":"Qirun","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,7,10]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20174850"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.08.009"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20226570"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2913588"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.05.017"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114481"},{"key":"e_1_2_12_7_2","doi-asserted-by":"crossref","unstructured":"GirshickR. 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