Zandamela, FrankRatshidaho, TerenceNicolls, FStoltz, George G2023-02-032023-02-032022-11Zandamela, F., Ratshidaho, T., Nicolls, F. & Stoltz, G.G. 2022. Cross-dataset performance evaluation of deep learning distracted driver detection algorithms. http://hdl.handle.net/10204/12602 .DOI https://doi.org/10.1051/matecconf/202237007002http://hdl.handle.net/10204/12602Deep learning has gained traction due its supremacy in terms of accuracy and ability to automatically learn features from input data. However, deep learning algorithms can sometimes be flawed due to many factors such as training dataset, parameters, and choice of algorithms. Few studies have evaluated the robustness of deep learning distracted driver detection algorithms. The studies evaluate the algorithms on a single dataset and do not consider cross-dataset performance. A problem arises because cross-dataset performance often implies model generalisation ability. Deploying a model in the real world without knowing its cross-dataset performance could lead to catastrophic events. The paper investigates the cross-dataset performance of deep learning distracted driver detection algorithms. Experimental results found reveal that deep learning distracted driver detection algorithms do not generalise well on unknown datasets for CNN models that use the whole image for prediction. The cross-dataset performance evaluations shed light on future research in developing robust deep learning distracted driver detection algorithms.FulltextenDashCamDeep learningDistracted Driver DetectionMachine learningConference PresentationZandamela, F., Ratshidaho, T., Nicolls, F., & Stoltz, G. G. (2022). Cross-dataset performance evaluation of deep learning distracted driver detection algorithms. http://hdl.handle.net/10204/12602Zandamela, Frank, Terence Ratshidaho, F Nicolls, and George G Stoltz. "Cross-dataset performance evaluation of deep learning distracted driver detection algorithms." <i>23rd Annual International RAPDASA Conference joined by RobMech, PRASA and CoSAAMI, Somerset-West, Cape Town, 9-11 November 2022</i> (2022): http://hdl.handle.net/10204/12602Zandamela F, Ratshidaho T, Nicolls F, Stoltz GG, Cross-dataset performance evaluation of deep learning distracted driver detection algorithms; 2022. http://hdl.handle.net/10204/12602 .TY - Conference Presentation AU - Zandamela, Frank AU - Ratshidaho, Terence AU - Nicolls, F AU - Stoltz, George G AB - Deep learning has gained traction due its supremacy in terms of accuracy and ability to automatically learn features from input data. However, deep learning algorithms can sometimes be flawed due to many factors such as training dataset, parameters, and choice of algorithms. Few studies have evaluated the robustness of deep learning distracted driver detection algorithms. The studies evaluate the algorithms on a single dataset and do not consider cross-dataset performance. A problem arises because cross-dataset performance often implies model generalisation ability. Deploying a model in the real world without knowing its cross-dataset performance could lead to catastrophic events. The paper investigates the cross-dataset performance of deep learning distracted driver detection algorithms. Experimental results found reveal that deep learning distracted driver detection algorithms do not generalise well on unknown datasets for CNN models that use the whole image for prediction. The cross-dataset performance evaluations shed light on future research in developing robust deep learning distracted driver detection algorithms. DA - 2022-11 DB - ResearchSpace DP - CSIR J1 - 23rd Annual International RAPDASA Conference joined by RobMech, PRASA and CoSAAMI, Somerset-West, Cape Town, 9-11 November 2022 KW - DashCam KW - Deep learning KW - Distracted Driver Detection KW - Machine learning LK - https://researchspace.csir.co.za PY - 2022 T1 - Cross-dataset performance evaluation of deep learning distracted driver detection algorithms TI - Cross-dataset performance evaluation of deep learning distracted driver detection algorithms UR - http://hdl.handle.net/10204/12602 ER -26140