4.2 RetinaNet model
Since the one-stage detector is not as accurate as the two-stage
detector, T. -Y. Lin et al. [22] proposed a new
loss function: focal loss and designed a dense detector: RetinaNet. By
reducing the weight of the easily classified samples to pay more
attention to the difficult-to-classify samples, the RetinaNet can
achieve not only the speed of the first-level detector, but also the
accuracy of the second-level detector. RetinaNet is essentially composed
of resnet residual network, FPN with two FCN sub-networks. The structure
of RetinaNet model is shown in Figure 7.