This　paper　introduces　a　trainable　deep　framework　called　DedistractedNet　for　recognizing　the　distracted　driving　behaviors　from　an　image.　In　contrast　to　other　conventional　strategies　that　use　physiological　sensors　or　on-board　diagnostics,　the　DedistractedNet　directly　profiles　the　features　of　driving　behaviors　in　the　image　based　on　the　deep　convolutional　neural　networks.　Experiment　results　manifest　that　the　DedistractedNet　achieves　superior　accuracy　than　those　of　other　baseline　CNN　methods.