The invention discloses a
pedestrian and
vehicle detection method and
system based on improved YOLOv3. According to the method, an improved YOLOv3 network based on
Darknet-33 is adopted as a main network to extract features; the cross-layer fusion and reuse of multi-scale features in the
backbone network are carried out by adopting a transmittable feature map scale reduction method; and then a feature
pyramid network is constructed by adopting a scale amplification method. In the training stage, a K-means clustering method is used for clustering the
training set, and the cross-to-parallel ratio of a prediction frame to a real frame is used as a similarity standard to select a priori frame; and then the BBox regression and the multi-
label classification are performed according to the
loss function. And in the detection stage, for all the detection frames, a non-maximum suppression method is adopted to remove redundant detection frames according to confidence scores and IOU values, and an optimal target object is predicted. According to the method, a
feature extraction network
Darknet-33 of feature map scale reduction fusion is adopted, a feature
pyramid is constructed through feature map scale amplification migration fusion, and a priori frame is selected through clustering, so that the speed and precision of the
pedestrian and
vehicle detection can be improved.