{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T07:47:01Z","timestamp":1768204021453,"version":"3.49.0"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:00:00Z","timestamp":1767312000000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52474176"],"award-info":[{"award-number":["52474176"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Detecting potholes in complex environments poses challenges such as varying illumination, shadows, and occlusions. Traditional methods often suffer from insufficient detection accuracy and poor real\u2010time performance. To enhance detection robustness without sacrificing inference speed, this paper adopts the RT\u2010DETR (Real\u2010Time Detection Transformer) framework\u2014which requires no NMS (Non\u2010Maximum Suppression) post\u2010processing and features an efficient hybrid encoder\u2014as its foundation. We propose the lightweight and efficient ARCH\u2010RTDETR detection model. The model introduces targeted enhancements to the backbone, feature\u2010fusion module, and multi\u2010scale architecture. Specifically, an AFGCA (Adaptive Fusion Global Context Attention) mechanism strengthens sensitivity to subtle cues; RepBN (Reparameterized Batch Normalization) is deeply integrated into the AIFI (Adaptive Instance Feature Integration) module to optimize feature distributions and increase multi\u2010scale representational capacity; and the proposed CA\u2010HSFPN (Coordinate Attention\u2010guided Hierarchical Scale Feature Pyramid Network) improves the effectiveness of cross\u2010scale feature fusion. Experiments on diverse datasets show that ARCH\u2010RTDETR achieves an average detection accuracy of 85%, outperforming the RT\u2010DETR baseline by 2.9%, while also improving detection precision and inference efficiency. These results indicate strong potential for deployment in intelligent transportation systems. This research provides a technical reference for small object detection, addressing the low efficiency of traditional manual inspections and the high detection latency of existing equipment in intelligent transportation systems, thereby offering a reliable technical solution for road safety assurance.<\/jats:p>","DOI":"10.1002\/cpe.70541","type":"journal-article","created":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T05:46:21Z","timestamp":1767419181000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>Enhanced<\/scp>\n                    Pothole Detection in Complex Environments Using\n                    <scp>ARCH\u2010RTDETR<\/scp>\n                    : A Lightweight and Efficient Approach"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2216-6798","authenticated-orcid":false,"given":"Zhihai","family":"Liu","sequence":"first","affiliation":[{"name":"College of Transportation, Shandong University of Science and Technology  Qingdao Shandong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruijie","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Transportation, Shandong University of Science and Technology  Qingdao Shandong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhao","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Transportation, Shandong University of Science and Technology  Qingdao Shandong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinfeng","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Transportation, Shandong University of Science and Technology  Qingdao Shandong China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1378600.1378605"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2017.2719865"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compenvurbsys.2017.12.005"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2019.00082"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2931297"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2014.6853659"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2933750"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2728680"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2017.08.017"},{"key":"e_1_2_10_11_1","first-page":"1","volume-title":"2018 IEEE International Conference on Consumer Electronics (ICCE)","author":"Lee S. 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