{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T11:16:45Z","timestamp":1792408605957,"version":"4.4.0"},"reference-count":41,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,8]],"date-time":"2023-04-08T00:00:00Z","timestamp":1680912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea","award":["2021R1C1C2007976"],"award-info":[{"award-number":["2021R1C1C2007976"]}]},{"name":"National Research Foundation of Korea","award":["202110260001"],"award-info":[{"award-number":["202110260001"]}]},{"name":"Gachon University research fund","award":["2021R1C1C2007976"],"award-info":[{"award-number":["2021R1C1C2007976"]}]},{"name":"Gachon University research fund","award":["202110260001"],"award-info":[{"award-number":["202110260001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Driver distraction is considered a main cause of road accidents, every year, thousands of people obtain serious injuries, and most of them lose their lives. In addition, a continuous increase can be found in road accidents due to driver\u2019s distractions, such as talking, drinking, and using electronic devices, among others. Similarly, several researchers have developed different traditional deep learning techniques for the efficient detection of driver activity. However, the current studies need further improvement due to the higher number of false predictions in real time. To cope with these issues, it is significant to develop an effective technique which detects driver\u2019s behavior in real time to prevent human lives and their property from being damaged. In this work, we develop a convolutional neural network (CNN)-based technique with the integration of a channel attention (CA) mechanism for efficient and effective detection of driver behavior. Moreover, we compared the proposed model with solo and integration flavors of various backbone models and CA such as VGG16, VGG16+CA, ResNet50, ResNet50+CA, Xception, Xception+CA, InceptionV3, InceptionV3+CA, and EfficientNetB0. Additionally, the proposed model obtained optimal performance in terms of evaluation metrics, for instance, accuracy, precision, recall, and F1-score using two well-known datasets such as AUC Distracted Driver (AUCD2) and State Farm Distracted Driver Detection (SFD3). The proposed model achieved 99.58% result in terms of accuracy using SFD3 while 98.97% accuracy on AUCD2 datasets.<\/jats:p>","DOI":"10.3390\/s23083835","type":"journal-article","created":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T03:24:18Z","timestamp":1681097058000},"page":"3835","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["EFFNet-CA: An Efficient Driver Distraction Detection Based on Multiscale Features Extractions and Channel Attention Mechanism"],"prefix":"10.3390","volume":"23","author":[{"given":"Taimoor","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam-si 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8435-6068","authenticated-orcid":false,"given":"Gyuho","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence Engineering, Chosun University, Gwangju 61452, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4632-8322","authenticated-orcid":false,"given":"Sokjoon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Smart Security, Gachon University, Seongnam-si 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Moslemi, N., Azmi, R., and Soryani, M. 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