Convolutional　neural　network　(CNN)　has　been　successfully　applied　to　image-based　crowd　density　estimation.　However,　large　computational　resources　are　required　in　previous　CNN-based　methods.　Therefore,　to　overcome　these　drawbacks,　this　paper　proposes　a　lightweight　crowd　density　map　estimation　architecture　with　Dilated　Inception　Convolution　Neural　Network　(DICNN).　The　proposed　method　not　only　extracts　scale-aware　informative　features,　but　also　effectively　reduces　the　number　of　parameters　of　the　CNN　architecture.　In　addition,　the　proposed　method　is　trained　along　with　DICNN　in　an　end-to-end　fashion　via　both　pixel-wise　Euclidean　distance　and　density-level　relevant　(DLR)　loss　for　global　optimization.　Extensive　experiments　on　several　publicly　available　datasets　have　shown　that　the　proposed　method　outperforms　state-of-the-art　approaches　in　almost　all　datasets　with　far　fewer　parameters.　©　2021　ACM.