YOLOv3 architecture.

(A) YOLOv3 pipeline with input image size 416×416 and 3 types of feature map (13×13×69, 26×26×69 and 52×52×69) as output; (B) the basic element of YOLOv3, Darknet_conv2D_BN_Leaky ("DBL" for short), is composed of one convolution layer, one batch normalization layer and one leaky relu layer.; (C) two "DBL" structures following with one "add" layer leads to residual-like unit ("ResUnit" for short); (D) several "ResUnit" with one zero padding layer and "DBL" structure forward generates residual-like block, "ResBlock" for short, which is the module element of Darknet-53; (E) some detection results of peripheral leukocyte using YOLOv3 approach, resize the 732×574 images to 416×416 size as input.