The invention discloses a deep learning pedestrian detection method based on an embedded terminal. The method comprises the steps that first, at a sample preparation stage, an existing automatic driving dataset is acquired, or manual annotations obtained after a fixed camera and a mobile camera shoot videos are collected; second, at a training stage, a large quantity of training images are used totrain parameters of a constructed convolutional neural network so as to complete detection feature learning; third, at a test stage, a large quantity of test images are input into the trained convolutional neural network, and a detection result is obtained; and fourth, at a porting stage, code level optimization and porting into the embedded terminal are performed. According to the method, the 18-layer convolutional neural network is adopted to perform pedestrian feature learning, and the method has an innovative advantage compared with a traditional machine learning method; and an optimization strategy for the embedded terminal is also proposed, the network scale and algorithm complexity are further reduced, and the method is suitable for ADAS function application.