  Counting people or objects with significantly varying scales and densities
has attracted much interest from the research community and yet it remains an
open problem. In this paper, we propose a simple but an efficient and effective
network, named DENet, which is composed of two components, i.e., a detection
network (DNet) and an encoder-decoder estimation network (ENet). We first run
DNet on an input image to detect and count individuals who can be segmented
clearly. Then, ENet is utilized to estimate the density maps of the remaining
areas, where the numbers of individuals cannot be detected. We propose a
modified Xception as an encoder for feature extraction and a combination of
dilated convolution and transposed convolution as a decoder. In the
ShanghaiTech Part A, UCF and WorldExpo'10 datasets, our DENet achieves lower
Mean Absolute Error (MAE) than those of the state-of-the-art methods.
